\Sort{
  Mode{on}
  Collation{mixed}
  SortTypeOrder{pattern,key,name}
  NameOrder{ascending}
  Key{author,_none}
  KeyOrder{ascending,nulls first,descending,nulls first}
  Pattern{author+editor: "doron"}
  PatternOrder{first}
  StringSort{ascending}
}
@ARTICLE{3o:csr00,
  author = {A. A. Petrenko and B. H. Jones and T. D. Dickey and P. Hamilton},
  year = 2000,
  title = {{Internal Tide Effects on a Sewage Plume at Sand Island, Hawaii}},
  journal = {Continental Shelf Research},
  volume = 20,
  pages = {1-13}
}
@article{Abolghasemi2020a,
title = {Demand forecasting in supply chain: The impact of demand volatility in the presence of promotion},
journal = {Computers \& Industrial Engineering},
volume = {142},
pages = {106380},
year = {2020},
issn = {0360-8352},
doi = {10.1016/j.cie.2020.106380},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0360835220301145},
author = {Mahdi Abolghasemi and Eric Beh and Garth Tarr and Richard Gerlach}
}
@article{Abolghasemi2020b,
title = {Demand forecasting in the presence of systematic events: Cases in capturing sales promotions},
journal = {International Journal of Production Economics},
volume = {230},
pages = {107892},
year = {2020},
issn = {0925-5273},
doi = {10.1016/j.ijpe.2020.107892},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0925527320302553},
author = {Mahdi Abolghasemi and Jason Hurley and Ali Eshragh and Behnam Fahimnia}
}
@article{Abolghasemi2022,
  title={Model selection in reconciling hierarchical time series},
  author={Mahdi Abolghasemi and Rob J. Hyndman and Evangelos Spiliotis and Christoph Bergmeir},
  journal={Machine Learning},
  year={2022},
  volume={111},
  pages={739-789}
}
@article{Aburto07,
  author = {L. Aburto and R. Weber},
  title = {Improved supply chain management based on hybrid demand forecasts},
  journal = {Applied Soft Computing},
  volume = {7},
  number = {1},
  pages = {126-144},
  year = {2007}
}
@article{Agrawal96,
  title = "Market share forecasting: An empirical comparison of artificial neural networks and multinomial logit model",
  journal = "Journal of Retailing",
  volume = "72",
  number = "4",
  pages = "383 - 407",
  year = "1996",
  issn = "0022-4359",
  doi = "10.1016/S0022-4359(96)90020-2",
  IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0022435996900202",
  author = "Deepak Agrawal and Christopher Schorling"
}
@INPROCEEDINGS{Akaike77,
  author = {H. Akaike},
  year = 1977,
  title = {On entropy maximization principle},
  booktitle = {Applications of Statistics},
  pages = {27–41},
  publisher = {Amsterdam: North-Holland},
  editor = {P. R. Krishnaiah}
}
@ARTICLE{Albertson:ascetrans70,
  author = {Albertson, M. L. and Dai, Y. B. and Jensen, R. A. and Rouse, H.},
  year = 1970,
  title = {{Diffusion of Submerged Jets}},
  journal = {ASCE Transactions},
  volume = 115,
  pages = {639-697}
}
@INPROCEEDINGS{Allen01,
  author = {P. G. Allen and R. Fildes},
  year = 2001,
  title = {{Econometric forecasting}},
  booktitle = {Principles of Forecasting: A Handbook for Researchers and Practitioners},
  pages = {271-327},
  publisher = {Kluwer Academic Publishing},
  editor = {J. Armstrong},
  address = {Boston}
}
@INPROCEEDINGS{Alon97,
  author = {I. Alon},
  year = 1997,
  title = {Forecasting aggregate retail sales: the Winters' model revisited},
  booktitle = {The 1997 Annual Proceedings. Midwest Decision Science Institute},
  pages = {234-236},
  editor = {J. C Goodale}
}
@article{Alon01,
  title = {{Forecasting aggregate retail sales: A comparison of artificial neural networks and traditional methods}},
  journal = {Journal of Retailing and Consumer Services},
  volume = {8},
  number = {3},
  pages = {147-156},
  year = {2001},
  issn = {0969-6989},
  doi = {10.1016/S0969-6989(00)00011-4},
  IGNOREurl = {http://www.sciencedirect.com/science/article/pii/S0969698900000114},
  author = {Ilan Alon and Min Qi and Robert J. Sadowski}
}
@INPROCEEDINGS{AMatosetal:Oceans2009a,
  author = {Bruno Ferreira and Miguel Pinto and An\'ibal Matos and Nuno Cruz},
  year = 2009,
  title = {Control of the MARES Autonomous Underwater Vehicle},
  booktitle = {Oceans 2009 Marine Technology and Ocean Science Conference},
  month = {September}
}
@INPROCEEDINGS{AMatosetal:Oceans2009b,
  author = {Bruno Ferreira and Miguel Pinto and An\'ibal Matos and Nuno Cruz},
  year = 2009,
  title = {Modeling and Motion Analysis of the MARES Autonomous Underwater Vehicle},
  booktitle = {Oceans 2009 Marine Technology and Ocean Science Conference},
  month = {September}
}
@INPROCEEDINGS{AMatosNCruz:Oceans99,
  author = {An\'ibal Matos and Nuno Cruz and Alfredo Martins and Fernando Lobo Pereira},
  year = 1999,
  title = {{Development and Implementation of a Low-Cost LBL Navigation System for an AUV}},
  booktitle = {{Proceedings of the MTS/IEEE Oceans'99 Conference}}
}
@INPROCEEDINGS{AMatosNCruz:Oceans2008,
  author = {An\'ibal Matos and Nuno Cruz},
  year = 2008,
  title = {The MARES AUV, a Modular Autonomous Robot for Environment Sampling},
  booktitle = {Oceans 2008 Marine Technology and Ocean Science Conference},
  month = {September}
}
@article{anderson2004model,
  title={Model selection and multi-model inference},
  author={Anderson, D and Burnham, K},
  journal={Second. NY: Springer-Verlag},
  volume={63},
  number={2020},
  pages={10},
  year={2004}
}
@INPROCEEDINGS{Anibal:oceans03,
  author = {An\'ibal Matos and Nuno Cruz and Fernando Lobo Pereira},
  year = 2003,
  title = {Post Mission Trajectory Smoothing for the Isurus {AUV}},
  booktitle = {Oceans 2003 Marine Technology and Ocean Science Conference},
  month = {September}
}
@ARTICLE{AnibalMatos:Controlo2000,
  author = {A. Matos and N. Cruz and F. L. Pereira},
  month = {October},
  year = 2000,
  title = {An Integrated System for Underwater Navigation and Tracking},
  journal = {Proceedings of Controlo 2000, 4th Portuguese Conference on Automatic Control}
}
@BOOK{Aoki87,
  author = {M. Aoki},
  year = 1987,
  title = {{State space modeling of time series}},
  publisher = {{Springer-Verlag, Berlin}}
}
@article{Arlot10,
  author = {S. Arlot and C. Alain},
  title = {A survey of cross-validation procedures for model selection},
  journal = {Statistics Surveys},
  volume = {4},
  pages = {40-79},
  year = {2010}
}
@article{Arunraj2015,
title = "A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting",
journal = "International Journal of Production Economics",
volume = "170",
pages = "321-335",
year = "2015",
issn = "0925-5273",
doi = "10.1016/j.ijpe.2015.09.039",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0925527315003783",
author = "Nari Sivanandam Arunraj and Diane Ahrens",
keywords = "SARIMAX, Seasonal Autoregressive Integrated Moving Average with External Variables, Sales Forecasting, Food Retail Industry, Perishable Foods, Quantile Regression",
abstract = "In the retail stage of a food supply chain, food waste and stock-outs occur mainly due to inaccurate forecasting of sales which leads to incorrect ordering of products. The time series sales in food retail industry are characterized by high volatility and skewness, which vary by time. So, the interval forecasts are required by the retail companies to set appropriate inventory policy (reorder point or safety stock level). This paper attempts to develop a seasonal autoregressive integrated moving average with external variables (SARIMAX) model to forecast daily sales of a perishable food. The process of fitting a SARIMAX model in this study involves: (i) the development of Seasonal Autoregressive Integrated Moving Average (SARIMA) model and (ii) combining the SARIMA model and the demand influencing factors using linear regression. As the SARIMAX using multiple linear regression (SARIMA-MLR) model produces only mean forecast, the possibility of underestimation and overestimation is very high due to high service level, peak, and sparse sales in food retail industry. Therefore, a hybrid SARIMA and Quantile Regression (SARIMA-QR) is developed to construct high and low quantile predictions. Instead of extrapolating the quantiles from the mean point forecasts of SARIMA-MLR model based on the assumption of normality, the SARIMA-QR model directly forecasts the quantiles. The developed SARIMA-MLR and SARIMA-QR models are applied in modeling and forecasting of sales data, i.e., the daily sales of banana from a discount retail store in Lower Bavaria, Germany. The results show that the SARIMA-MLR and -QR models yield better forecasts at out-sample data when compared to seasonal naïve forecasting, traditional SARIMA, and multi-layered perceptron neural network (MLPNN) models. Unlike the SARIMA-MLR model, the SARIMA-QR model provides better prediction intervals and a deep insight into the effects of demand influencing factors for different quantiles."
}
@article{Athanasopoulos09,
title = "Hierarchical forecasts for Australian domestic tourism",
journal = "International Journal of Forecasting",
volume = "25",
number = "1",
pages = "146 - 166",
year = "2009",
issn = "0169-2070",
doi = "10.1016/j.ijforecast.2008.07.004",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0169207008000691",
author = "George Athanasopoulos and Roman A. Ahmed and Rob J. Hyndman",
keywords = "Australia, Exponential smoothing, Hierarchical forecasting, Innovations state space models, Optimal combination forecasts, Top-down method, Tourism demand",
abstract = "In this paper we explore the hierarchical nature of tourism demand time series and produce short-term forecasts for Australian domestic tourism. The data and forecasts are organized in a hierarchy based on disaggregating the data according to geographical regions and purposes of travel. We consider five approaches to hierarchical forecasting: two variations of the top-down approach, the bottom-up method, a newly proposed top-down approach where top-level forecasts are disaggregated according to the forecasted proportions of lower level series, and a recently proposed optimal combination approach. Our forecast performance evaluation shows that the top-down approach based on forecast proportions and the optimal combination method perform best for the tourism hierarchies we consider. By applying these methods, we produce detailed forecasts of the Australian domestic tourism market."
}
@article{Athanasopoulos17,
title = "Forecasting with temporal hierarchies",
journal = "European Journal of Operational Research",
volume = "262",
number = "1",
pages = "60 - 74",
year = "2017",
issn = "0377-2217",
doi = "10.1016/j.ejor.2017.02.046",
IGNOREurl = "https://www.sciencedirect.com/science/article/abs/pii/S0377221717301911",
author = "George Athanasopoulos and Rob J. Hyndman and Nikolaos Kourentzes and Fotios Petropoulos"
}
@incollection{Athanasopoulos2020,
author = "George Athanasopoulos and Puwasala Gamakumara and Anastasios Panagiotelis and Rob J. Hyndman and Mohamed Affan",
title = "Hierarchical Forecasting",
editor = "P. Fuleky",
booktitle = "Macroeconomic Forecasting in the Era of Big Data",
publisher = "Springer, Cham",
volume = "52",
chapter = "21",
pages = "689--719",
year = "2020",
issn = "978-3-030-31150-6",
doi = "10.1007/978-3-030-31150-6_21",
IGNOREurl = "https://www.springer.com/gp/book/9783030311490"
}
@article{ATHANASOPOULOS2022,
title = {On the evaluation of hierarchical forecasts},
journal = {International Journal of Forecasting},
year = {2022},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2022.08.003},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207022001121},
author = {George Athanasopoulos and Nikolaos Kourentzes},
keywords = {Aggregation, Coherence, Hierarchical time series, Multiple objectives, Reconciliation},
abstract = {The aim of this paper is to provide a thinking road-map and a practical guide to researchers and practitioners working on hierarchical forecasting problems. Evaluating the performance of hierarchical forecasts comes with new challenges stemming from both the structure of the hierarchy and the application context. We discuss several relevant dimensions for researchers and analysts: the scale and units of the time series, the issue of intermittency, the forecast horizon, the importance of multiple evaluation windows and the multiple objective decision context. We conclude with a series of practical recommendations.}
}
@article{Au08,
author = {K. F. Au and T. M. Choi and Y. Yu},
title = {Fashion retail forecasting by evolutionary neural networks},
journal = {International Journal of Production Economics},
volume = {114},
number = {2},
pages = {615-630},
year = {2008}
}
@article{Badorf2020,
title = {The impact of daily weather on retail sales: An empirical study in brick-and-mortar stores},
journal = {Journal of Retailing and Consumer Services},
volume = {52},
pages = {101921},
year = {2020},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2019.101921},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698919303236},
author = {Florian Badorf and Kai Hoberg},
keywords = {Retailing, Weather, Brick-and-mortar stores, Regression analysis},
abstract = {In this study, we examine the influence of weather on daily sales in brick-and-mortar retailing using empirical data for 673 stores. We develop a random coefficient model that considers non-linear effects and seasonal differences using different weather parameters. In the ex-post analysis using historic weather data, we quantify the explanatory power of weather information on daily sales, identify store-specific effects and analyze the influence of specific sales themes. We find that the weather has generally a complex effect on daily sales while the magnitude and the direction of the weather effect depend on the store location and the sales theme. The effect on daily sales can be as high as 23.1\% based on the store location and as high as 40.7\% based on the sales theme. We also find that the impact of extreme bad and good weather occurrences can be misestimated by traditional models that do not consider non-linear effects. In the ex-ante analysis, we analyze if weather forecasts can be used to improve the daily sales forecast. We show that including weather forecast information improves sales forecast accuracy up to seven days ahead. However, the improvement of the forecast accuracy diminishes with a higher forecast horizon.}
}
@InProceedings{Bandara2019,
author = {Kasun Bandara and Peibei Shi and Christoph Bergmeir and Hansika Hewamalage and Quoc Tran and Brian Seaman},
title= {Sales Demand Forecast in E-commerce Using a Long Short-Term Memory Neural Network Methodology},
booktitle = {Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science},
volume = {11955},
year = {2019},
publisher = {Springer International Publishing},
address = {Cham},
pages = {462-474},
}
@article{Bandara2020,
title = {Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach},
journal = {Expert Systems with Applications},
volume = {140},
pages = {112896},
year = {2020},
issn = {0957-4174},
doi = {https://doi.org/10.1016/j.eswa.2019.112896},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0957417419306128},
author = {Kasun Bandara and Christoph Bergmeir and Slawek Smyl},
keywords = {Big data forecasting, RNN, LSTM, Time series clustering, Neural networks},
abstract = {With the advent of Big Data, nowadays in many applications databases containing large quantities of similar time series are available. Forecasting time series in these domains with traditional univariate forecasting procedures leaves great potentials for producing accurate forecasts untapped. Recurrent neural networks (RNNs), and in particular Long Short Term Memory (LSTM) networks, have proven recently that they are able to outperform state-of-the-art univariate time series forecasting methods in this context, when trained across all available time series. However, if the time series database is heterogeneous, accuracy may degenerate, so that on the way towards fully automatic forecasting methods in this space, a notion of similarity between the time series needs to be built into the methods. To this end, we present a prediction model that can be used with different types of RNN models on subgroups of similar time series, which are identified by time series clustering techniques. We assess our proposed methodology using LSTM networks, a widely popular RNN variant, together with various clustering algorithms, such as kMeans, DBScan, Partition Around Medoids (PAM), and Snob. Our method achieves competitive results on benchmarking datasets under competition evaluation procedures. In particular, in terms of mean sMAPE accuracy it consistently outperforms the baseline LSTM model, and outperforms all other methods on the CIF2016 forecasting competition dataset.}
}
@article{Bandara2021,
title = {A fast and scalable ensemble of global models with long memory and data partitioning for the M5 forecasting competition},
journal = {International Journal of Forecasting},
year = {2021},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2021.11.004},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207021001783},
author = {Kasun Bandara and Hansika Hewamalage and Rakshitha Godahewa and Puwasala Gamakumara}
}
@article{BANDARA2021108148,
title = {Improving the accuracy of global forecasting models using time series data augmentation},
journal = {Pattern Recognition},
volume = {120},
pages = {108148},
year = {2021},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2021.108148},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0031320321003356},
author = {Kasun Bandara and Hansika Hewamalage and Yuan-Hao Liu and Yanfei Kang and Christoph Bergmeir},
keywords = {Time series forecasting, Global forecasting models, Data augmentation, Transfer learning, RNN},
abstract = {Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques. In most cases, GFMs are implemented using deep neural networks, and in particular Recurrent Neural Networks (RNN), which require a sufficient amount of time series to estimate their numerous model parameters. However, many time series databases have only a limited number of time series. In this study, we propose a novel, data augmentation based forecasting framework that is capable of improving the baseline accuracy of the GFM models in less data-abundant settings. We use three time series augmentation techniques: GRATIS, moving block bootstrap (MBB), and dynamic time warping barycentric averaging (DBA) to synthetically generate a collection of time series. The knowledge acquired from these augmented time series is then transferred to the original dataset using two different approaches: the pooled approach and the transfer learning approach. When building GFMs, in the pooled approach, we train a model on the augmented time series alongside the original time series dataset, whereas in the transfer learning approach, we adapt a pre-trained model to the new dataset. In our evaluation on competition and real-world time series datasets, our proposed variants can significantly improve the baseline accuracy of GFM models and outperform state-of-the-art univariate forecasting methods.}
}
@article{barrow2018impact,
  title={The impact of special days in call arrivals forecasting: A neural network approach to modelling special days},
  author={Barrow, Devon and Kourentzes, Nikolaos},
  journal={European Journal of Operational Research},
  volume={264},
  number={3},
  pages={967--977},
  year={2018},
  publisher={Elsevier},
  doi={10.1016/j.ejor.2016.07.015}
}
@article{Barrow2020,
title = {Automatic robust estimation for exponential smoothing: Perspectives from statistics and machine learning},
journal = {Expert Systems with Applications},
volume = {160},
pages = {113637},
year = {2020},
issn = {0957-4174},
doi = {10.1016/j.eswa.2020.113637},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0957417420304619},
author = {Devon Barrow and Nikolaos Kourentzes and Rickard Sandberg and Jacek Niklewski}
}
@ARTICLE{batchelor:52,
  author = {Batchelor, G. K.},
  year = 1952,
  title = {{Diffusion in a Field of Homogeneous Turbulence. II. The Relative
          Motion of Particles}},
  journal = {Proc. Camb. Phil. Soc.},
  volume = 48,
  pages = {345-362}
}
@INPROCEEDINGS{Bellingham92,
  author = {J.G. Bellingham and C. Goudey and T. R. Consi and C.
           Chryssostomidis},
  year = 1992,
  title = {{A Small Long Range Vehicle for Deep Ocean Exploration}},
  booktitle = {Proceedings of the International Offshore and Polar Engineering Conference, San Francisco},
  pages = {151-159}
}
@ARTICLE{Bellingham97,
  author = {Bellingham, J.G.},
  year = 1997,
  title = {{New Oceanographic Uses of Autonomous Underwater Vehicles}},
  journal = {Marine Technology Society Journal},
  volume = 31,
  number = 3,
  pages = {34-47}
}
@article{Bemmaor99,
  author = {Albert C. Bemmaor and Philip Hans Franses and Jeanine Kippers},
  journal = {Marketing Letters},
  number = {1},
  pages = {87--100},
  publisher = {Springer},
  title = {Estimating the Impact of Displays and Other Merchandising Support on Retail Brand Sales: Partial Pooling with Examples},
  volume = {10},
  year = {1999},
  IGNOREurl = {https://www.jstor.org/stable/40216522}
}
@article{Benidis2022,
author = {Benidis, Konstantinos and Rangapuram, Syama Sundar and Flunkert, Valentin and Wang, Yuyang and Maddix, Danielle and Turkmen, Caner and Gasthaus, Jan and Bohlke-Schneider, Michael and Salinas, David and Stella, Lorenzo and Aubet, Fran\c{c}ois-Xavier and Callot, Laurent and Januschowski, Tim},
title = {Deep Learning for Time Series Forecasting: Tutorial and Literature Survey},
year = {2022},
issue_date = {July 2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {55},
number = {6},
issn = {0360-0300},
IGNOREurl = {https://doi.org/10.1145/3533382},
doi = {10.1145/3533382},
abstract = {Deep learning based forecasting methods have become the methods of choice in many applications of time series prediction or forecasting often outperforming other approaches. Consequently, over the last years, these methods are now ubiquitous in large-scale industrial forecasting applications and have consistently ranked among the best entries in forecasting competitions (e.g., M4 and M5). This practical success has further increased the academic interest to understand and improve deep forecasting methods. In this article we provide an introduction and overview of the field: We present important building blocks for deep forecasting in some depth; using these building blocks, we then survey the breadth of the recent deep forecasting literature.},
journal = {ACM Comput. Surv.},
month = {dec},
articleno = {121},
numpages = {36},
keywords = {forecasting, Time series, neural networks}
}
@BOOK{benilde:microbiologia98,
  author = {Benilde Mendes},
  editor = {Lidel},
  year = 1998,
  title = {{Microbiologia}},
  volume = {I},
  note = {ISBN 972-757-024-0},
  publisher = {{Lidel - Edi\c c\~oes T\'ecnicas}},
  address = {Lisboa}
}
@ARTICLE{Beutel:2012,
  author = {Anna-Lena Beutel and Stefan Minner},
  year = 2012,
  title = {{Safety stock planning under causal demand forecasting}},
  journal = {International Journal of Production Economics},
  volume = 140,
  pages = {637-645}
}
@ARTICLE{Bigg:1967,
  author = {P.H. Bigg},
  year = 1967,
  title = {{Density of Water in SI Units Over the Range 0-40�C}},
  journal = {British Journal of Applied Physics},
  volume = 18,
  pages = {521-525}
}
@article{Billah2006,
title = {Exponential smoothing model selection for forecasting},
journal = {International Journal of Forecasting},
volume = {22},
number = {2},
pages = {239-247},
year = {2006},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2005.08.002},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S016920700500107X},
author = {Baki Billah and Maxwell L. King and Ralph D. Snyder and Anne B. Koehler},
keywords = {Model selection, Exponential smoothing, Information criteria, Prediction, Forecast validation},
abstract = {Applications of exponential smoothing to forecasting time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to selecting the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentage error. A second approach is to rely on the most appropriate general case of the three methods. For annual series this is trend corrected exponential smoothing: for sub-annual series it is the seasonal adaptation of trend corrected exponential smoothing. The rationale for this approach is that a general method automatically collapses to its nested counterparts when the pertinent conditions pertain in the data. A third approach may be based on an information criterion when maximum likelihood methods are used in conjunction with exponential smoothing to estimate the smoothing parameters. In this paper, such approaches for selecting the appropriate forecasting method are compared in a simulation study. They are also compared on real time series from the M3 forecasting competition. The results indicate that the information criterion approaches provide the best basis for automated method selection, the Akaike information criteria having a slight edge over its information criteria counterparts.}
}
@article{Bojer2021,
title = {Kaggle forecasting competitions: An overlooked learning opportunity},
journal = {International Journal of Forecasting},
volume = {37},
number = {2},
pages = {587-603},
year = {2021},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2020.07.007},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207020301114},
author = {Casper Solheim Bojer and Jens Peder Meldgaard},
keywords = {Time series methods, M competitions, Business forecasting, Forecast accuracy, Machine learning methods, Benchmarking, Time series visualization, Forecasting competition review},
abstract = {We review the results of six forecasting competitions based on the online data science platform Kaggle, which have been largely overlooked by the forecasting community. In contrast to the M competitions, the competitions reviewed in this study feature daily and weekly time series with exogenous variables, business hierarchy information, or both. Furthermore, the Kaggle data sets all exhibit higher entropy than the M3 and M4 competitions, and they are intermittent. In this review, we confirm the conclusion of the M4 competition that ensemble models using cross-learning tend to outperform local time series models and that gradient boosted decision trees and neural networks are strong forecast methods. Moreover, we present insights regarding the use of external information and validation strategies, and discuss the impacts of data characteristics on the choice of statistics or machine learning methods. Based on these insights, we construct nine ex-ante hypotheses for the outcome of the M5 competition to allow empirical validation of our findings.}
}
@MISC{boston:mwrasite,
  author = {MWRA},
  title = {{Massachusetts Water Resources Authority, Boston}},
  url = {http://www.mwra.com}
}
@TECHREPORT{bostonmwra:dilutionapril01,
  author = {C. D. Hunt and W. S. Steinhauer and A. D. Mansfield and C. A.
           Albro and P. J. W. Roberts and W. R. Geyer and M. J. Mickelson},
  year = 2002,
  title = {{Massachusetts Water Resources Authority Effluent Outfall Dilution: April 2001}},
  institution = {Boston: Massachusetts Water Resources Authority},
  type = {{Report ENQUAD} 2002-06, 69 p. plus appendices}
}
@TECHREPORT{bostonmwra:dilutionjuly01,
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title = {Social media marketing for businesses: Organic promotions of web-links on Facebook},
journal = {Journal of Business Research},
volume = {135},
pages = {49-65},
year = {2021},
issn = {0148-2963},
doi = {10.1016/j.jbusres.2021.06.020},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S014829632100432X},
author = {Yash Chawla and Grzegorz Chodak},
keywords = {Social media marketing, Facebook marketing, Organic promotion, Web links, External links},
abstract = {The effectiveness of social media marketing is a topic of great interest for researchers as well as marketers. To enrich the literature regarding the effectiveness of various types of posts with a web-link, we designed and conducted an experiment on Facebook (FB). This experiment was conducted in a real business environment, through the FB fanpage of a Polish e-commerce store. The observations were analyzed using multiple linear regression and metrics adapted to this experiment from the literature. The results show that a web-link placed in the comments of an FB post, instead of the caption, is more lucrative. It is also shown that, based on the aims of the campaign, such metrics can give valuable information about the optimal time for posting, as well as the interval between posts.}
}
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@article{Cicatiello2016,
title = {The value of food waste: An exploratory study on retailing},
journal = {Journal of Retailing and Consumer Services},
volume = {30},
pages = {96-104},
year = {2016},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2016.01.004},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698916300078},
author = {Clara Cicatiello and Silvio Franco and Barbara Pancino and Emanuele Blasi},
keywords = {Food loss, Food waste, Surplus retail food, Environmental impact, Social value of food, Economic value of food, Surplus food recovery},
abstract = {Retailers are increasingly concerned with the sustainability of their business. Food waste is a major sustainability issue: 90 million tons of food are wasted in the EU every year. The production of much of this waste is directly linked to the food chain operations, included those performed at the retail stage. The literature on food waste has mainly focused so far on the quantification of the total food lost along the supply chain. However, the stage of retail has long been neglected. This paper attempts to partly fill this research gap, with the aim of measuring the extent of food waste in retailing as well as its environmental, social and economic value. To do so, we analyse the results of a food waste recovery project held in an Italian supermarket and, by drawing on the data collected in this case study, we perform an evaluation of the value of the food wasted. The results show that the extent of food waste in retailing is certainly considerable, both in terms of quantity and economic value. Moreover, we found evidence that it may be greatly reduced, with a significant limitation of its environmental impact and, through the mechanism of recovery, it may even generate social benefits. Despite the many limitations of such preliminary research, the results provide useful information for retailers aiming to develop strategies against food waste in the context of improving the sustainability of their business.}
}
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  pages={1523-1545},
  doi={10.1109/TIT.2005.844059}}
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  author = {Stephanie Clark and Rob J Hyndman and Dan Pagendam and Louise M Ryan}
}
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 ISSN = {07322399, 1526548X},
 IGNOREurl = {http://www.jstor.org/stable/193174},
 author = {Lee G. Cooper and Penny Baron and Wayne Levy and Michael Swisher and Paris Gogos},
 journal = {Marketing Science},
 number = {3},
 pages = {301-316},
 publisher = {INFORMS},
 title = {PromoCast™: A New Forecasting Method for Promotion Planning},
 volume = {18},
 year = {1999},
 doi = {10.1287/mksc.18.3.301}
}
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}
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  abstract = {With all the hype around efficient consumer response (ECR) and the brave new world of technologies, one would believe that retail out‐of‐stocks have gone down over the last ten years. That is wrong. Retailers have been struggling with considerable out‐of‐stocks for decades – with little evidence of improvement. A similar wrong belief is that shoppers are also still unwilling to accept low service levels. In fact, increasingly, consumers switch brands when they do not find the brand they wanted. But retailers must be wary, because the results of our research show that increasingly shoppers switch stores quickly and may never come back. So, who is to blame? The supply chain. And where to tackle it? On the shop floor. Over the past two years, we have conducted a major, worldwide study of the extent, causes, and consumer responses to out‐of‐stocks in the fast‐moving consumer goods industry. In this article, we report these findings and provide insight to solving this chronic industry problem.}
}
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eprint = {10.1057/jors.1972.50}
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}
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  author = "Andrey Davydenko and Robert Fildes",
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          Interdisciplinary Measurements in Massachusetts and Cape Cod Bayes}},
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}
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  pages = {190-204},
  year = {2007}
}
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}
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  title = {{Proposal for a Directive of the European Parliament and of the Council Concerning the Quality of Bathing Water}},
  howpublished = {{Commission of the European Communities}}
}
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}
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}
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  note = {pp. 2959-2966},
  howpublished = {{Di\'ario da Rep\'ublica - S\'erie I-A}}
}
@MISC{dl:17201,
  author = {{DL 172/2001}},
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  title = {{Decreto-Lei N.172/2001 de 26 de Maio (Zonas Sens\'{\i}veis)}},
  howpublished = {Di\'ario da Rep\'ublica - I S\'erie}
}
@MISC{dl:23698,
  author = {{DL 236/98}},
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  note = {pp. 3676-3722},
  howpublished = {{Di\'ario da Rep\'ublica - S\'erie I-A}}
}
@MISC{dl:26199,
  author = {{DL 261/99}},
  year = 1999,
  title = {{Decreto-Lei N.61/99 de 7 de Julho (Zonas Menos Sens\'{\i}veis das Regi\~oes Aut\'onomas)}},
  howpublished = {Di\'ario da Rep\'ublica - I S\'erie}
}
@MISC{dl:34898,
  author = {{DL 348/98}},
  year = 1998,
  title = {{Decreto-Lei N.48/98 de 11 de Novembro (transposi\c c\~ao)}},
  howpublished = {{Di\'ario da Rep\'ublica - S\'erie-IA}}
}
@article{Doganis08,
  author = {P. Doganis and E. Aggelogiannaki and H. Sarimveis},
  title = {A combined model predictive control and time series forecasting framework for production-inventory systems},
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  volume = {46},
  number = {24},
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  year = {2008}
}
@article{Dokumentov2021,
  author = {Alex Dokumentov and Rob J Hyndman},
  title = {{STR: Seasonal-Trend decomposition using Regression}},
  journal = {INFORMS Journal on Data Science.},
  volume = {To appear},
  number = {},
  pages = {},
  year = {2021}
}
@Inbook{Duncan2001,
author="Duncan, George T.
and Gorr, Wilpen L.
and Szczypula, Janusz",
editor="Armstrong, J. Scott",
title="Forecasting Analogous Time Series",
bookTitle="Principles of Forecasting: A Handbook for Researchers and Practitioners",
year="2001",
publisher="Springer US",
address="Boston, MA",
pages="195--213",
abstract="Organizations that use time-series forecasting regularly, generally use it for many products or services. Among the variables they forecast are groups of analogous time series (series that follow similar, time-based patterns). Their covariation is a largely untapped source of information that can improve forecast accuracy. We take the Bayesian pooling approach to drawing information from analogous time series to model and forecast a given time series. In using Bayesian pooling, we use data from analogous time series as multiple observations per time period in a group-level model. We then combine estimated parameters of the group model with conventional time-series-model parameters, using so-called weights shrinkage. Major benefits of this approach are that it (1) requires few parameters for estimation; (2) builds directly on conventional time-series models; (3) adapts to pattern changes in time series, providing rapid adjustments and accurate model estimates; and (4) screens out adverse effects of outlier data points on time-series model estimates. For practitioners, we provide the terms, concepts, and methods necessary for a basic understanding of Bayesian pooling and the conditions under which it improves upon conventional time-series methods. For researchers, we describe the experimental data, treatments, and factors needed to compare the forecast accuracy of pooling methods. Last, we present basic principles for applying pooling methods and supporting empirical results. Conditions favoring pooling include time series with high volatility and outliers. Simple pooling methods are more accurate than complex methods, and we recommend manual intervention for cases with few time series.",
isbn="978-0-306-47630-3",
doi="10.1007/978-0-306-47630-3_10",
url="https://doi.org/10.1007/978-0-306-47630-3_10"
}
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  title = {Aggregate versus subaggregate models in local area forecasting},
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  publisher = {Taylor & Francis},
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  note = {doi:10.1080/01621459.1976.10481478},
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}
@ARTICLE{economopoulou:jee01,
  author = {Maria A. Economopoulou and Alexander P. Economopoulos},
  year = 2001,
  title = {{Graphical Sizing and Analysis of Ocean Outfalls with Buoyant
          Plumes}},
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  volume = 127,
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}
@InProceedings{Erven15,
  author="van Erven, Tim and Cugliari, Jairo",
  title="Game-Theoretically Optimal Reconciliation of Contemporaneous Hierarchical Time Series Forecasts",
  year="2015",
  editor = {Antoniadis A., Poggi JM., Brossat X.},
  publisher = {Springer},
  booktitle = {Modeling and Stochastic Learning for Forecasting in High Dimensions},
  note = {Lecture Notes in Statistics, vol 217. doi: 10.1007/978-3-319-18732-7\_15},
  pages="297--317",
  doi = {10.1007/978-3-319-18732-7_15}
}
@article{Ettouzani12,
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  month = {04},
  pages = {213-243},
  title = {Examining Retail on Shelf Availability: Promotional Impact and a Call for Research},
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  journal = {International Journal of Physical Distribution & Logistics Management},
  doi = {10.1108/09600031211225945}
}
@TECHREPORT{EuParliament12,
  author = {{European Parliament}},
  title = {Parliament calls for urgent measures to halve food wastage in the EU},  
  institution = {European Commission},
  year = 2012,
  IGNOREurl = {http://www.europarl.europa.eu/news/en/pressroom/content/20120118IPR35648/
html/Parliament-calls-for-urgent-measures-to-halve-food-wastage-in-the-EU}
}
@article{FallahTehrani2016,
title = {Enhanced predictive models for purchasing in the fashion field by using kernel machine regression equipped with ordinal logistic regression},
journal = {Journal of Retailing and Consumer Services},
volume = {32},
pages = {131-138},
year = {2016},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2016.05.008},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698915301648},
author = {Ali {Fallah Tehrani} and Diane Ahrens},
keywords = {Ordinal logistic regression, Fashion products, Sales forecasting, Kernel machines},
abstract = {Identifying the products which are highly sold in the fashion apparel industry is one of the challenging tasks, which leads to reduce the write off and increases the revenue. In fact, beyond of sales forecasting in general a crucial question remains whether a product may sell well or not. Assuming three classes as substantial, middle and inconsiderable, the forecasting problem comes down to a classification problem, where the task is to predict the class of a product. In this research, we present a probabilistic approach to identify the class of fashion products in terms of sale. Thereafter, we combine kernel machines with a probabilistic approach to empower the performance of kernel machines and eventually to make use of it to predicting the number of sales. The proposed approach is more robust to outliers (in the case of highly sold products) and in addition uses prior knowledge, hence it serves more reliable results. In order to verify the proposed approach, we conducted several experiments on a real data extracted from an apparel retailer in Germany.}
}
@INPROCEEDINGS{faisst:conf90,
  author = {William K. Faisst and Rhys M. McDonald and Tom Noon and Geoff
           Marsh},
  editor = {H. Chang, New York, San Diego},
  month = {July 30 - August 3},
  year = 1990,
  title = {{Iona Outfall, Plume Characterization Study}},
  booktitle = {Proceedings 1990 National Conference on Hydraulic Engineering},
  publisher = {ASCE}
}
@TECHREPORT{fan:67,
  author = {L. N. Fan},
  year = 1967,
  title = {{Turbulent Buoyant Jets Into Stratified or Flowing Ambient Fluids}},
  institution = {California Institute of Technology, Pasadena, California},
  type = {{W. M. Keck Laboratory Report KH-R-15}}
}
@Article{FactoMineR,
   title = {{FactoMineR}: A Package for Multivariate Analysis},
   author = {S\'ebastien L\^e and Julie Josse and Fran\c{c}ois Husson},
   journal = {Journal of Statistical Software},
   year = {2008},
   volume = {25},
   number = {1},
   pages = {1--18},
   doi = {10.18637/jss.v025.i01},
  }
@Manual{feasts2022,
  title = {feasts: Feature Extraction and Statistics for Time Series},
  author = {Mitchell O'Hara-Wild and Rob Hyndman and Earo Wang},
  year = {2022},
  note = {http://feasts.tidyverts.org/, https://github.com/tidyverts/feasts/},
}
@ARTICLE{Fernandes00,
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  year = 2000,
  title = {{Fish do not Avoid Survey Vessels}},
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  pages = {35-36}
}
@article{Fildes07,
  author = {R. A. Fildes and P. Goodwin},
  title = {{Against your better judgment? How organizations can improve their use of management judgment in forecasting}},
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  volume = {37},
  pages = {570-576},
  year = {2007}
}
@article{fildes2009effective,
  title={Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning},
  author={Fildes, Robert and Goodwin, Paul and Lawrence, Michael and Nikolopoulos, Konstantinos},
  journal={International journal of forecasting},
  volume={25},
  number={1},
  pages={3--23},
  year={2009},
  publisher={Elsevier},
  doi = {10.1016/j.ijforecast.2008.11.010}
}
@BOOK{Fildes12,
  author = {J. K. Ord and R. Fildes},
  year = 2012,
  title = {{Principles of business forecasting}},
  publisher = {{South-Western College Pub}}
}
@article{Fildes15,
  title = "Simple versus complex selection rules for forecasting many time series",
  journal = "Journal of Business Research",
  volume = "68",
  number = "8",
  pages = "1692 - 1701",
  year = "2015",
  issn = "0148-2963",
  doi = "10.1016/j.jbusres.2015.03.028",
  IGNOREurl = "10.1016/j.jbusres.2015.03.028",
  note = {doi:10.1016/j.jbusres.2015.03.028},
  author = "Robert Fildes and Fotios Petropoulos"
}
@article{Fildes2019,
title = {Retail forecasting: Research and practice},
journal = {International Journal of Forecasting},
year = {2022},
volume = {38},
pages = {1283 - 1318},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2019.06.004},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S016920701930192X},
author = {Robert Fildes and Shaohui Ma and Stephan Kolassa}
}
@article{Fildes2021,
title = {Post-script—Retail forecasting: Research and practice},
journal = {International Journal of Forecasting},
year = {2021},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2021.09.012},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207021001618},
author = {Robert Fildes and Stephan Kolassa and Shaohui Ma},
keywords = {COVID-19, Disruption, Structural change, Instability, Omni-retailing, Online retail, Machine learning},
abstract = {This note updates the 2019 review article “Retail forecasting: Research and practice” in the context of the COVID-19 pandemic and the substantial new research on machine-learning algorithms, when applied to retail. It offers new conclusions and challenges for both research and practice in retail demand forecasting.}
}
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}
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  year = 2001,
  title = {{Chemical Plume Mapping with an Autonomous Underwater Vehicle}},
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  address = {Hawaii, USA}
}
@article{Fliedner99,
title = "An investigation of aggregate variable time series forecast strategies with specific subaggregate time series statistical correlation",
journal = {Computers \& Operations Research},
volume = "26",
number = "10",
pages = "1133 - 1149",
year = "1999",
issn = "0305-0548",
doi = "10.1016/S0305-0548(99)00017-9",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0305054899000179",
author = "Gene Fliedner",
abstract = "In order to meet demand for family-based forecasting systems, firms such as IBM and American Software, Inc. offer software packages capable of forecasting demand for individual items and families of items. These systems, sometimes referred to as constrained or hierarchical forecasting (HF), are based upon a user defined strategy for aggregating items into families. HF systems are for determining forecasts for items and their respective families following either a direct strategy, which utilizes the contemporaneous demand values to compute a forecast, or a derived strategy, which determines forecasts for items by proportioning a parent family forecast. The objectives of HF systems include improved forecast performance and a reduction in the overall forecasting burden. HF strategies rely upon the premise that forecasts for families of items are more accurate than forecasts for individual items. Research literature indicates that HF system forecast performance is dependent upon the statistical nature of the items comprising the aggregated family. This literature further suggests that families be formed such that important information (demand patterns) exhibited within the item-level time series’ does not get lost when summing these series to define the aggregated group time series. Therefore, families of HF systems would logically be defined to provide a high degree of homogeneity, or positive correlation among family members. This strategy suggests that a group of homogenous items will tend to result in an aggregate time series which exhibits (or at least does not obscure) data patterns present in time series of the component items comprising the family. This study is an examination of strategies and guidelines for specifying aggregated families within HF systems. In particular, simulation is used as the vehicle to measure forecast performance at the aggregate (family) level. Varying degrees of systematically controlled, statistical correlation between subaggregate (item) time series comprising the forecast families is investigated using all four combinations of two forecast smoothing techniques and two forecast strategies. The results of this study support the research literature conclusion that higher positive correlation does indeed lead to improved forecast performance at the aggregate level. However, the results also indicate that higher negative correlation also leads to improved forecast performance at the aggregate level. An additional observation suggests that direct forecasts of an aggregate variable are more accurate than derived forecasts. Scope and purpose Many firms rely upon demand forecasts to conduct short-term planning activities such as purchasing, production planning and inventory control. These forecasts are often generated weekly. Firms that maintain a large product line consisting of thousands of items are faced with an especially burdensome forecast task. Consequently, firms such as IBM and American Software, Inc. offer software packages capable of forecasting demand for families of items. Family-based forecast systems attempt to improve forecast performance while offering a reduction in the overall forecasting burden. This study is an examination of strategies for specifying families within large forecast systems."
}
@article{Fliedner01,
  author = {Gene Fliedner},
  title = {{Hierarchical forecasting: issues and use guidelines}},
  journal = {Industrial Management \& Data Systems},
  volume = {101},
  pages = {5-12},
  year = {2001}
}
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}
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  year = {2003}
}
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author = {James Franklin},
year = 2005,
title = {{The elements of statistical learning: data mining, inference and prediction}},
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volume = 27,
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issn = {0343-6993},
IGNOREurl = {https://doi.org/10.1007/BF02985802},
doi = {10.1007/BF02985802}
}
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   abstract = {We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge regression) and mixtures of the two (the elastic net). The algorithms use cyclical coordinate descent, computed along a regularization path. The methods can handle large problems and can also deal efficiently with sparse features. In comparative timings we find that the new algorithms are considerably faster than competing methods.},
   issn = {1548-7660},
   pages = {1--22},
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   doi = {10.18637/jss.v033.i01}
}
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title = {The M4 forecasting competition – A practitioner’s view},
journal = {International Journal of Forecasting},
volume = {36},
number = {1},
pages = {156-160},
year = {2020},
note = {M4 Competition},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2019.02.013},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207019301189},
author = {Chris Fry and Michael Brundage}
}
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  author = {E. S. Gardner},
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  pages = {1-28},
  year = 1985
}
@article{Gardner06,
  author = {E. S. Gardner},
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  pages = {637-666},
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}
@InProceedings{Gasthaus2019,
  title = 	 {Probabilistic Forecasting with Spline Quantile Function RNNs},
  author =       {Gasthaus, Jan and Benidis, Konstantinos and Wang, Yuyang and Rangapuram, Syama Sundar and Salinas, David and Flunkert, Valentin and Januschowski, Tim},
  booktitle = 	 {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
  pages = 	 {1901--1910},
  year = 	 {2019},
  editor = 	 {Chaudhuri, Kamalika and Sugiyama, Masashi},
  volume = 	 {89},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {16--18 Apr},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v89/gasthaus19a/gasthaus19a.pdf},
  url = 	 {https://proceedings.mlr.press/v89/gasthaus19a.html},
  abstract = 	 {In this paper, we propose a flexible method for probabilistic modeling with conditional quantile functions using monotonic regression splines. The shape of the spline is parameterized by a neural network whose parameters are learned by minimizing the continuous ranked probability score. Within this framework, we propose a method for probabilistic time series forecasting, which combines the modeling capacity of recurrent neural networks with the flexibility of a spline-based representation of the output distribution. Unlike methods based on parametric probability density functions and maximum likelihood estimation, the proposed method can flexibly adapt to different output distributions without manual intervention. We empirically demonstrate the effectiveness of the approach  on synthetic and real-world data sets.}
}
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  year = 2001,
  title = {{GeoR: A package for geostatistical analysis}},
  journal = {R-NEWS},
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	note = {{ISBN: ISSN 1609-3631}},
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  publisher={Cambridge University Press},
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}
@article{Gilliland2020,
title = {The value added by machine learning approaches in forecasting},
journal = {International Journal of Forecasting},
volume = {36},
number = {1},
pages = {161-166},
year = {2020},
note = {M4 Competition},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2019.04.016},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207019301165},
author = {Michael Gilliland}
}
@article{GluonTS2020,
  author  = {Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneider
    and Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddix
    and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and
    Ali Caner Türkmen and Yuyang Wang},
  title   = {{GluonTS: Probabilistic and Neural Time Series Modeling in Python}},
  journal = {Journal of Machine Learning Research},
  year    = {2020},
  volume  = {21},
  number  = {116},
  pages   = {1--6},
  url     = {http://jmlr.org/papers/v21/19-820.html}
}
@article{Godahewa2021,
title = {Ensembles of localised models for time series forecasting},
journal = {Knowledge-Based Systems},
volume = {233},
pages = {107518},
year = {2021},
issn = {0950-7051},
doi = {10.1016/j.knosys.2021.107518},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0950705121007802},
author = {Rakshitha Godahewa and Kasun Bandara and Geoffrey I. Webb and Slawek Smyl and Christoph Bergmeir}
}
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  author = {P. Goovaerts},
  editor = { Oxford University Press},
  year = 1997,
  title = {{Geostatistics for natural resources evaluation, Applied Geostatistics Series}},
  note = {{ISBN13: 9780195115383, ISBN10: 0195115384}},
}
@article{Gordon97,
  title = "Top-down or bottom-up: which is the best approach to forecasting?",
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  pages = "13–16",
  year = "1997",
  author = "Gordon, T. P. and Morris, J. S. and Dangerfield, B. J."
}
@BOOK{Grace:book78,
  author = {Robert A. Grace},
  title = {{Marine Outfall Systems: Planning, Design, and Construction}},
  publisher = {Prentice-Hall, Inc., Englewood Cliffs, New Jersey}
}
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  year = 2000,
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}
@article{Gross90,
author = {Gross, Charles W. and Sohl, Jeffrey E.},
title = {Disaggregation methods to expedite product line forecasting},
journal = {Journal of Forecasting},
volume = {9},
number = {3},
pages = {233-254},
keywords = {Product line, Forecasting, Disaggregational methods, Time series analysis, Composite root mean square error differential},
doi = {10.1002/for.3980090304},
IGNOREurl = {https://onlinelibrary.wiley.com/doi/abs/10.1002/for.3980090304},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/for.3980090304},
abstract = {Abstract This paper addresses the issue of forecasting individual items within a product line; where each line includes several independent but closely related products. The purpose of the research was to reduce the overall forecasting burden by developing and assessing schemes of disaggregating forecasts of a total product line to the related individual items. Measures were developed to determine appropriate disaggregated methodologies and to compare the forecast accuracy of individual product forecasts versus disaggregated totals. Several of the procedures used were based upon extensions of the combination of forecast research and applied to disaggregations of total forecasts of product lines. The objective was to identify situations when it was advantageous to produce disaggregated forecasts, and if advantageous, which method of disaggregation to utilize. This involved identification of the general conceptual characteristics within a set of product line data that might cause a disaggregation method to produce relatively accurate forecasts. These conceptual characteristics provided guidelines for forecasters on how to select a disaggregation method and under what conditions a particular method is applicable.},
year = {1990}
}
@TECHREPORT{Gruen02,
  author = {T.W. Gruen and D.S. Corsten and S. Bharadwaj},
  year = 2002,
  title = {{Retail out-of-stock: a worldwide examination of extent, causes and consumer responses. The Food Business Forum.}}
}
@article{Grunfeld60,
  author = {Y. Grunfeld and Z. Griliches},
  title = {Is aggregation necessarily bad?},
  journal = {Review of Economics and Statistics},
  volume = {42},
  number = {1},
  year = {1960},
  pages = {1-13}
}
@MISC{GuidanceMonitoring:DQA,
  author = {{Water Framework Directive Common Implementation Strategy Working Group 2.7 Monitoring}},
  year = 2003,
  title = {{Guidance on Monitoring for the Water Framework Directive, Final Version 23 January 2003}}
}
@article{GurAli2009,
title = "{SKU} demand forecasting in the presence of promotions",
journal = "Expert Systems with Applications",
volume = "36",
number = "10",
pages = "12340-12348",
year = "2009",
issn = "0957-4174",
doi = "10.1016/j.eswa.2009.04.052",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0957417409004035",
author = "Ozden {Gur Ali} and Serpil Sayın and Tom {van Woensel} and Jan Fransoo",
keywords = "Demand forecasting, Time series, Machine learning, Pooling, Domain knowledge, Promotions",
abstract = "Promotions and shorter life cycles make grocery sales forecasting more difficult, requiring more complicated models. We identify methods of increasing complexity and data preparation cost yielding increasing improvements in forecasting accuracy, by varying the forecasting technique, the input features and model scope on an extensive SKU-store level sales and promotion time series from a European grocery retailer. At the high end of data and technique complexity, we propose using regression trees with explicit features constructed from sales and promotion time series of the focal and related SKU-store combinations. We observe that data pooling almost always improves model performance. The results indicate that simple time series techniques perform very well for periods without promotions. However, for periods with promotions, regression trees with explicit features improve accuracy substantially. More sophisticated input is only beneficial when advanced techniques are used. We believe that our approach and findings shed light into certain questions that arise while building a grocery sales forecasting system."
}
@article{GurAli2016,
title = "{Multi-period-ahead forecasting with residual extrapolation and information sharing — Utilizing a multitude of retail series}",
journal = "International Journal of Forecasting",
volume = "32",
number = "2",
pages = "502-517",
year = "2016",
issn = "0169-2070",
doi = "10.1016/j.ijforecast.2015.03.011",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0169207015000850",
author = "Ozden {Gur Ali} and Efe Pinar",
keywords = "Multivariate time series, Sales forecasting, Panel data, Data mining, Regression, Retail, Multi-period ahead forecast",
abstract = "Multi-period sales forecasts are important inputs for operations at retail chains with hundreds of stores, and many different formats, customer segments and categories. In addition to the effects of seasonality, holidays and marketing, correlated random disturbances also affect sales across stores that share common characteristics. We propose a novel method, Two-Stage Information Sharing that takes advantage of this challenging complexity. In this method, segment-specific panel regressions with seasonality and marketing variables pool the data, in order to provide better parameter estimates. The residuals are then extrapolated non-parametrically using features that are constructed from the last twelve months of observations from the focal and related category-store time series. The final forecast combines the extrapolated residuals with the forecasts from the first stage. Working with the extensive dataset of a leading Turkish retailer, we show that this method significantly outperforms both panel regression models (mixed model) with an AR(1) error structure and the autoregressive distributed lags (ADL) model, as well as the univariate exponential smoothing (Winters’) method. The further out the prediction, the greater the improvement."
}
@book{Hamilton94,
  author = {Hamilton, {James Douglas}},
  isbn = {0691042896},
  pagetotal = {XIV, 799},
  publisher = {Princeton University Press},
  title = {Time series analysis},
  year = 1994
}
@BOOK{Harvey89,
  author = {A. C. Harvey},
  title = {Forecasting, structural time series models and the Kalman filter},
  publisher = {Cambridge University Press},
  year = 1989,
  doi = {10.1017/CBO9781107049994}
}
@BOOK{Hanssens03,
  author = {Dominique M. Hanssens and Leonard J. Parsons and Randall L. Schultz},
  title = {Market response models: Econometric and time series analysis},
  series = {International Series in Quantitative Marketing},
  publisher = {Springer US},
  volume = 12,
  year = 2001,
  issn = {0923-6716},
  doi = {10.1007/b109775}
}
@BOOK{Hastie98,
  author = {Trevor Hastie and Robert Tibshirani and Jerome Friedman},
  year = 1998,
  title = {The Elements of Statistical Learning},
  subtitle = {Data Mining, Inference, and Prediction},
  series = {Springer Series in Statistics},
  issn = {0172-7397},
  publisher = {Springer-Verlag New York},
  doi = {10.1007/978-0-387-84858-7}
}
@article{Henriques2013,
author = {Henriques, Teresa and Gonçalves, Hernâni and Antunes, Luís and Matias, Mara and Bernardes, João and Costa-Santos, Cristina},
title = {Entropy and compression: two measures of complexity},
journal = {Journal of Evaluation in Clinical Practice},
volume = {19},
number = {6},
pages = {1101-1106},
keywords = {medical informatics},
doi = {https://doi.org/10.1111/jep.12068},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/jep.12068},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/jep.12068},
abstract = {Abstract Rationale, aims and objectives Traditional complexity measures are used to capture the amount of structured information present in a certain phenomenon. Several approaches developed to facilitate the characterization of complexity have been described in the related literature. Fetal heart rate (FHR) monitoring has been used and improved during the last decades. The importance of these studies lies on an attempt to predict the fetus outcome, but complexity measures are not yet established in clinical practice. In this study, we have focused on two conceptually different measures: Shannon entropy, a probabilistic approach, and Kolmogorov complexity, an algorithmic approach. The main aim of the current investigation was to show that approximation to Kolmogorov complexity through different compressors, although applied to a lesser extent, may be as useful as Shannon entropy calculated by approximation through different entropies, which has been successfully applied to different scientific areas. Methods To illustrate the applicability of both approaches, two entropy measures, approximate and sample entropy, and two compressors, paq8l and bzip2, were considered. These indices were applied to FHR tracings pertaining to a dataset composed of 48 delivered fetuses with umbilical artery blood (UAB) pH in the normal range (pH ≥ 7.20), 10 delivered mildly acidemic fetuses and 10 moderate-to-severe acidemic fetuses. The complexity indices were computed on the initial and final segments of the last hour of labour, considering 5- and 10-minute segments. Results In our sample set, both entropies and compressors were successfully utilized to distinguish fetuses at risk of hypoxia from healthy ones. Fetuses with lower UAB pH presented significantly lower entropy and compression indices, more markedly in the final segments. Conclusions The combination of these conceptually different measures appeared to present an improved approach in the characterization of different pathophysiological states, reinforcing the theory that entropies and compressors measure different complexity features. In view of these findings, we recommend a combination of the two approaches.},
year = {2013}
}

@article{Hewamalage22,
title = {Global models for time series forecasting: A Simulation study},
journal = {Pattern Recognition},
volume = {124},
pages = {108441},
year = {2022},
issn = {0031-3203},
doi = {https://doi.org/10.1016/j.patcog.2021.108441},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0031320321006178},
author = {Hansika Hewamalage and Christoph Bergmeir and Kasun Bandara},
keywords = {Time series forecasting, Global forecasting models, Time series simulation, Data generating processes},
abstract = {The recent advances in Big Data have opened up the opportunity to develop competitive Global Forecasting Models (GFM) that simultaneously learn from many time series. Although, the concept of series relatedness has been heavily exploited with GFMs to explain their superiority over local statistical benchmarks, this concept remains largely under-investigated in an empirical setting. Hence, this study attempts to explore the factors that affect GFM performance, by simulating a number of datasets having controllable characteristics. The factors being controlled are along the homogeneity/heterogeneity of series, the complexity of patterns in the series, the complexity of forecasting models, and the lengths/number of series. We simulate time series from simple Data Generating Processes (DGP), such as Auto Regressive (AR), Seasonal AR and Fourier Terms to complex DGPs, such as Chaotic Logistic Map, Self-Exciting Threshold Auto-Regressive and Mackey-Glass Equations. We perform experiments on these datasets using Recurrent Neural Networks (RNN), Feed-Forward Neural Networks, Pooled Regression models and Light Gradient Boosting Models (LGBM) built as GFMs, and compare their performance against standard statistical forecasting techniques. Our experiments demonstrate that with respect to GFM performance, relatedness is closely associated with other factors such as the availability of data, complexity of data and the complexity of the forecasting technique used. Also, techniques such as RNNs and LGBMs having complex non-linear modelling capabilities, when built as GFMs are competitive methods under challenging forecasting scenarios such as short series, heterogeneous series and having minimal prior knowledge of the data patterns.}
}
@article{Hoch95,
  author = {Stephen J. Hoch and Byung-Do Kim and Alan L. Montgomery and Peter E. Rossi},
  title ={Determinants of Store-Level Price Elasticity},
  journal = {Journal of Marketing Research},
  volume = {32},
  number = {1},
  pages = {17-29},
  year = {1995},
  doi = {10.1177/002224379503200104},
  IGNOREURL = {10.1177/002224379503200104},
  eprint = {10.1177/002224379503200104},
}
@article{Hochreiter1997,
    author = {Hochreiter, Sepp and Schmidhuber, Jürgen},
    title = "{Long Short-Term Memory}",
    journal = {Neural Computation},
    volume = {9},
    number = {8},
    pages = {1735-1780},
    year = {1997},
    month = {11},
    abstract = "{Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.}",
    issn = {0899-7667},
    doi = {10.1162/neco.1997.9.8.1735}
}

@BOOK{hollander:book15,
  author = {Myles Hollander and Douglas A. Wolfe and Eric Chicken},
  year = 2015,
  title = {{Nonparametric Statistical Methods}},
  publisher = {John Wiley \& Sons, Inc.: Hoboken, NJ, USA},
  doi = {10.1002/9781119196037}
}
@article{Holzer2020,
title = {The effect of time-varying factors on promotional activity in the German milk market},
journal = {Journal of Retailing and Consumer Services},
volume = {55},
pages = {102090},
year = {2020},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2020.102090},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S096969891931505X},
author = {Patrick Sebastian Holzer},
keywords = {Promotions, Food retailing, Timing},
abstract = {Promotional sales are an essential pricing tool in the marketing mix of food retailers, which induced numerous studies on the topic. However, few studies have considered time-varying factors of promotional activity in retailing. This study analyzes the effect of time-varying factors on promotional activity in German food retailing using an extensive scanner data set of weekly milk prices. I used a zero-truncated negative binomial model to estimate the time that elapsed between promotional sales. The study shows that promotional activity is high in times of peak demand, when recent unit sales are low, and when the regular price has recently been changed. The results provide compelling evidence that time-varying factors influence the retailer's pricing behavior.}
}
@ARTICLE{hopfinger:jgr87,
  author = {E. J. Hopfinger},
  year = 1987,
  title = {{Turbulence in Stratified Fluids: A Review}},
  journal = {Journal of Geophysical Research},
  volume = 92,
  number = {C5},
  pages = {5287-5303}
}
@article{HOSSAIN2020,
title = {Revisiting customer analytics capability for data-driven retailing},
journal = {Journal of Retailing and Consumer Services},
volume = {56},
pages = {102187},
year = {2020},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2020.102187},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698919314523},
author = {Md Afnan Hossain and Shahriar Akter and Venkata Yanamandram},
keywords = {Customer analytics capability, Data-driven retailing, Outside-in marketing, Systematic literature review, Thematic analysis},
abstract = {Customer analytics is one of the most dominant strategic weapons in today's competitive retail environment. In spite of its strategic importance, there is scant attention to investigating customer analytics capabilities in the retail context. Drawing on a systematic literature review and thematic analysis, this study proposes a multidimensional customer analytics capability model by identifying relevant dimensions and sub-dimensions in retail settings. The principal contribution of this study is that the model links a customer analytics perspective to a resource-based view (RBV)-capability of the retailers by proposing six customer analytics capability dimensions and twelve sub-dimensions in the spectrum of market orientation and technology orientation. The customer analytics capability dimensions depict three crucial themes of marketing, such as value creation (offering capability and personalization capability), value delivery (distribution capability and communication capability), and value management (data management capability and data protection capability). By incorporating this capability dimensions, practitioners will likely be able to engage customers and enhance customer equity.}
}
@Manual{hts18,
  title = {{hts}: Hierarchical and Grouped Time Series},
  author = {Rob Hyndman and Alan Lee and Earo Wang and Shanika Wickramasuriya},
  year = {2018},
  note = {R package version 5.1.5},
  url = {https://CRAN.R-project.org/package=hts}
}
@article{Huang2014,
title = {The value of competitive information in forecasting {FMCG} retail product sales and the variable selection problem},
journal = "European Journal of Operational Research",
volume = "237",
number = "2",
pages = "738-748",
year = "2014",
issn = "0377-2217",
doi = "10.1016/j.ejor.2014.02.022",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0377221714001374",
author = "Tao Huang and Robert Fildes and Didier Soopramanien",
keywords = "Forecasting, Business analytics, OR in marketing, Retailing, Promotions, Competitive information",
abstract = "Sales forecasting at the UPC level is important for retailers to manage inventory. In this paper, we propose more effective methods to forecast retail UPC sales by incorporating competitive information including prices and promotions. The impact of these competitive marketing activities on the sales of the focal product has been extensively documented. However, competitive information has been surprisingly overlooked by previous studies in forecasting UPC sales, probably because of the problem of too many competitive explanatory variables. That is, each FMCG product category typically contains a large number of UPCs and is consequently associated with a large number of competitive explanatory variables. Under such a circumstance, time series models can easily become over-fitted and thus generate poor forecasting results. Our forecasting methods consist of two stages. In the first stage, we refine the competitive information. We identify the most relevant explanatory variables using variable selection methods, or alternatively, pool information across all variables using factor analysis to construct a small number of diffusion indexes. In the second stage, we specify the Autoregressive Distributed Lag (ADL) model following a general to specific modelling strategy with the identified most relevant competitive explanatory variables and the constructed diffusion indexes. We compare the forecasting performance of our proposed methods with the industrial practice method and the ADL model specified exclusively with the price and promotion information of the focal product. The results show that our proposed methods generate substantially more accurate forecasts across a range of product categories."
}
@article{Huang2019,
title = "Forecasting retailer product sales in the presence of structural change",
journal = "European Journal of Operational Research",
volume = "279",
number = "2",
pages = "459-470",
year = "2019",
issn = "0377-2217",
doi = "10.1016/j.ejor.2019.06.011",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0377221719304862",
author = "Tao Huang and Robert Fildes and Didier Soopramanien",
keywords = "Forecasting, OR in marketing, Analytics, Retailing",
abstract = "Grocery retailers need accurate sales forecasts at the Stock Keeping Unit (SKU) level to effectively manage their inventory. Previous studies have proposed forecasting methods which incorporate the effect of various marketing activities including prices and promotions. However, their methods have overlooked that the effects of the marketing activities on product sales may change over time. Therefore, these methods may be subject to the structural change problem and generate biased and less accurate forecasts. In this study, we propose more effective methods to forecast retailer product sales which take into account the problem of structural change. Based on data from a well-known US retailer, we show that our methods outperform conventional forecasting methods that ignore the possibility of such changes."
}
@article{HUBER2017,
title = {Cluster-based hierarchical demand forecasting for perishable goods},
journal = {Expert Systems with Applications},
volume = {76},
pages = {140-151},
year = {2017},
issn = {0957-4174},
doi = {10.1016/j.eswa.2017.01.022},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0957417417300313},
author = {Jakob Huber and Alexander Gossmann and Heiner Stuckenschmidt},
keywords = {Predictive analytics, Decision support, Perishable fresh food, Hierarchical demand forecasting, Cluster analysis},
abstract = {Demand forecasting is of particular importance for retailers in the context of supply chains of perishable goods and fresh food. Such goods are daily produced and delivered as they need to be provided as fresh as possible and quickly deteriorate. Demand underestimation and overestimation negatively affect the revenues of the retailer. Stock-outs have an undesired impact on consumers while unsold items need to be discarded at the end of the day. We propose a DSS that supports day-to-operations by providing hierarchical forecasts at different organizational levels based on most recent point-of-sales data. It identifies article clusters that are used to extend the hierarchy based on intra-day sales pattern. We apply multivariate ARIMA models to forecast the daily demand to support operational decisions. We evaluate the approach with point-of-sales data of an industrialized bakery chain and show that it is possible to increase the availability while limiting the loss at the same time. The cluster analysis reveals that substitutable items have similar intra-day sales pattern which makes it reasonable to forecast the demand at an aggregated level. The accuracy of top-down forecasts is comparable to direct forecasts which allows reducing the computational costs.}
}
@article{Hyndman2002,
title = {A state space framework for automatic forecasting using exponential smoothing methods},
journal = {International Journal of Forecasting},
volume = {18},
number = {3},
pages = {439-454},
year = {2002},
issn = {0169-2070},
doi = {10.1016/S0169-2070(01)00110-8},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207001001108},
author = {Rob J Hyndman and Anne B Koehler and Ralph D Snyder and Simone Grose}
}
@article{Hyndman06,
title = {Another look at measures of forecast accuracy},
journal = {International Journal of Forecasting},
volume = {22},
number = {4},
pages = {679-688},
year = {2006},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2006.03.001},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207006000239},
author = {Rob J. Hyndman and Anne B. Koehler}
}
@BOOK{Hyndman08a,
  author = {R. J. Hyndman and A. B. Koehler and J. K. Ord and R. D. Snyder},
  publisher = {Berlin: Springer-Verlag},
  title = {{Forecasting with exponential smoothing: The state space approach}},
  series = {Springer Series in Statistics},
  year = 2008,
  issn = {0172-7397},
  doi = {10.1007/978-3-540-71918-2}
}
@Manual{Hyndman08b,
  title = {{forecast}: Forecasting functions for time series and linear models. {R} package version 8.15},
  author = {Rob Hyndman and George Athanasopoulos and Christoph Bergmeir and Gabriel Caceres and Leanne Chhay and Mitchell O'Hara-Wild and Fotios Petropoulos and Slava Razbash and Earo Wang and Farah Yasmeen and R Core Team and Ross Ihaka and Daniel Reid and David Shaub and Yuan Tang and Zhenyu Zhou},
  year = {2021},
  url = {http://pkg.robjhyndman.com/forecast},
}
@Article{Hyndman08c,
  title = {Automatic time series forecasting: the forecast package for {R}},
  author = {Rob J Hyndman and Yeasmin Khandakar},
  journal = {Journal of Statistical Software},
  volume = {27},
  number = {3},
  pages = {1--22},
  year = {2008},
  IGNOREurl = {https://www.jstatsoft.org/article/view/v027i03},
  doi = {10.18637/jss.v027.i03}
}
@article{Hyndman11,
  author = {Rob J. Hyndman and Roman A. Ahmed and George Athanasopoulos and Han L. Shang},
  title = {Optimal combination forecasts for hierarchical time series},
  journal = {Computational Statistics and Data Analysis},
  volume = {55},
  number = {9},
  pages = {2579-2589},
  year = {2011},
  note = {doi:10.1016/j.csda.2011.03.006},
  doi = {10.1016/j.csda.2011.03.006}
}
@BOOK{Hyndman13,
  author = {R. J. Hyndman and G. Athanasopoulos},
  publisher = {OTexts: Melbourne, Australia},
  year = 2021,
  title = {{Forecasting: principles and practice, 3rd edition}},
  url = {https://OTexts.com/fpp3}
}
@article{Hyndman14,
  author = {Rob J Hyndman and George Athanasopoulos},
  title = {Optimally reconciling forecasts in a hierarchy},
  journal = {Foresight: The International Journal of Applied Forecasting},
  volume = {Fall},
  number = {35},
  pages = {42-48},
  year = {2014}
}
@INPROCEEDINGS{Hyndman2015,
author={Hyndman, Rob J. and Wang, Earo and Laptev, Nikolay},
booktitle={2015 IEEE International Conference on Data Mining Workshop (ICDMW)},
title={Large-Scale Unusual Time Series Detection},
year={2015},
volume={},
number={},
pages={1616-1619},
doi={10.1109/ICDMW.2015.104}
}
@article{Hyndman16,
title = "Fast computation of reconciled forecasts for hierarchical and grouped time series",
journal = "Computational Statistics & Data Analysis",
volume = "97",
pages = "16 - 32",
year = "2016",
issn = "0167-9473",
doi = "10.1016/j.csda.2015.11.007",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S016794731500290X",
author = "Rob J. Hyndman and Alan J. Lee and Earo Wang",
keywords = "Combining forecasts, Grouped time series, Hierarchical time series, Reconciling forecasts, Weighted least squares",
abstract = "It is shown that the least squares approach to reconciling hierarchical time series forecasts can be extended to much more general collections of time series with aggregation constraints. The constraints arise due to the need for forecasts of collections of time series to add up in the same way as the observed time series. It is also shown that the computations involved can be handled efficiently by exploiting the structure of the associated design matrix, or by using sparse matrix routines. The proposed algorithms make forecast reconciliation feasible in business applications involving very large numbers of time series."
}
@BOOK{Hyndman18,
  author = {R. J. Hyndman and G. Athanasopoulos},
  publisher = {Online Open-access Textbooks},
  year = 2018,
  title = {{Forecasting: principles and practice}},
  url = {https://OTexts.org/fpp2/},
  note = {https://OTexts.com/fpp2/}  
}
@Manual{Hyndman2023,
  title = {tsfeatures: Time Series Feature Extraction},
  author = {Rob Hyndman and Yanfei Kang and Pablo Montero-Manso and Thiyanga Talagala and Earo Wang and Yangzhuoran Yang and Mitchell O'Hara-Wild},
  year = {2023},
  note = {R package version 1.1.0.9000},
  url = {https://pkg.robjhyndman.com/tsfeatures/},
}
@MANUAL{INAG:Derrogacoes98,
  author = {{INAG}},
  month = {Mar\c co},
  year = 1998,
  title = {{Linhas de Orienta\c c\~ao Metodol\'ogia para a Elabora\c c\~ao de Estudos T\'ecnicos Necess\'arios para Cumprir o Art. 7 do D.L. 152/97. Descargas em Zonas Menos Sens\'{\i}veis}},
  address = {Lisboa},
  organization = {{Instituto da \'Agua}. Minist\'erio do Ambiente}
}
@MISC{INAG:RelSituUE02,
  author = {{INAG}},
  year = 2002,
  title = {{Aplica\c c\~ao da Directiva 91/271/CEE do Conselho, de 21 de Maio de 1991 Relativa ao Tratamento de \'Aguas Residuais Urbanas, Alterada pela Directiva 98/15/CE da Comiss\~ao, de 27 de Fevereiro de 1998 - Relat\'orio de Situa\c c\~ao em 31 de Dezembro de 2000, Portugal}},
  howpublished = {Instituto da \'Agua}
}
@MISC{Inag:site,
  author = {INAG},
  year = 2004,
  title = {{Instituto da \'Agua}},
  url = {{http://www.inag.pt}}
}
@BOOK{Isaaks,
  author = {E. H. Isaaks and  R. M. Srivastava},
  editor = {Oxford University Press},
  year = 1989,
  title = {{Applied geostatistics}},
  note = {{ISBN 0-19-505012-6-ISBN 0-19-505013-4 (pbk.)}},
  address = {New York Oxford}
}
@ARTICLE{ivey:jpo91,
  author = {Ivey, G. N. and Imberger, J.},
  year = 1991,
  title = {{On the Nature of Turbulence in a Stratified Fluid. Part 1. The Energetics of Mixing}},
  journal = {Journal of Physical Oceanography},
  volume = 21,
  number = 5,
  pages = {650-658}
}
@misc{Januschowski2018,
title= {Now available in amazon sagemaker: Deepar algorithm for more accurate time series forecasting},
author = "{Januschowski, T., Arpin, D., Salinas, D., Flunkert, V., Gasthaus, J.,
Stella, L., Vazquez, P.}",
howpublished = "\url{https://aws.amazon.com/blogs/machine-learning/now-available-in-amazon-sagemaker-deepar-algorithm-for-more-accurate-time-series-forecasting/}",
year = {2018},
note = {Accessed: 2023-03-06}
}
@article{Januschowski2020,
title = {Criteria for classifying forecasting methods},
journal = {International Journal of Forecasting},
volume = {36},
number = {1},
pages = {167-177},
year = {2020},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2019.05.008},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207019301529},
author = {Tim Januschowski and Jan Gasthaus and Yuyang Wang and David Salinas and Valentin Flunkert and Michael Bohlke-Schneider and Laurent Callot}
}
@article{JEON2022,
title = {Robust recurrent network model for intermittent time-series forecasting},
journal = {International Journal of Forecasting},
volume = {38},
number = {4},
pages = {1415-1425},
year = {2022},
note = {Special Issue: M5 competition},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2021.07.004},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207021001151},
author = {Yunho Jeon and Sihyeon Seong},
keywords = {M5 accuracy competition, Time-series forecasting, DeepAR, Tweedie, Ensemble, Model selection},
abstract = {This paper describes a deep-learning-based time-series forecasting method that was ranked third in the accuracy challenge of the M5 competition. We solved the problem using a deep-learning approach based on DeepAR, which is an auto-regressive recurrent network model conditioned on historical inputs. To address the intermittent and irregular characteristics of sales demand, we modified the training procedure of DeepAR; instead of using actual values for the historical inputs, our model uses values sampled from a trained distribution and feeds them to the network as past values. We obtained the final result using an ensemble of multiple models to make a robust and stable prediction. To appropriately select a model for the ensemble, each model was evaluated using the average weighted root mean squared scaled error, calculated for all levels of a wide range of past periods.}
}
@ARTICLE{jirka:jfm79,
  author = {Jirka, Gerhard H. and Harleman, Donald R. F.},
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  title = {{Stability and Mixing of a Vertical Plane Buoyant Jet in Confined Depth}},
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}
@BOOK{Jolliffe2002,
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  edition = {2nd},
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}
@INPROCEEDINGS{jones:coastalzone91,
  author = {Burton H. Jones and Libe Washburn and Yicun Wu},
  year = 1991,
  title = {{The Dispersion of Ocean Outfall Plumes: Physical and Biological Dynamics}},
  booktitle = {Coastal Zone '91, Proceedings of 7th Symposium on Coastal \& Ocean Management},
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  publisher = {ASCE},
  address = {Long Beach, CA, USA}
}
@INPROCEEDINGS{jones:oceans01,
  author = {Burton H. Jones and Arthur Barnett and George L. Robertson},
  month = {November 5-8},
  year = 2001,
  title = {{Towed Mapping of the Effluent Plume from a Coastal Ocean Outfall}},
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}
@INPROCEEDINGS{Joseph07,
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  year = 2007,
  title = {{Forecasting aggregate sales with interest rates using multiple neural network architectures}},
  booktitle = {Part IV: Financial Engineering and Data Mining from: Intelligent Engineering Systems Through Artificial Neural Networks},
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  editor = {C. H. Dagli}
}
@ARTICLE{Kalyanam07,
author = {Kirthi Kalyanam and Sharad Borle and Peter Boatwright},
year = 2007,
title = {{Deconstructing Each Item’s Category Contribution}},
journal = {Marketing Science},
volume = 26,
number = 3,
pages = {327–341},
doi = {10.1287/mksc.1070.0270}
}
@article{KANG2021719,
title = {Déjà vu: A data-centric forecasting approach through time series cross-similarity},
journal = {Journal of Business Research},
volume = {132},
pages = {719-731},
year = {2021},
issn = {0148-2963},
doi = {10.1016/j.jbusres.2020.10.051},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0148296320307232},
author = {Yanfei Kang and Evangelos Spiliotis and Fotios Petropoulos and Nikolaos Athiniotis and Feng Li and Vassilios Assimakopoulos},
keywords = {Forecasting, Dynamic time warping, M competitions, Time series similarity, Empirical evaluation},
abstract = {Accurate forecasts are vital for supporting the decisions of modern companies. Forecasters typically select the most appropriate statistical model for each time series. However, statistical models usually presume some data generation process while making strong assumptions about the errors. In this paper, we present a novel data-centric approach — ‘forecasting with cross-similarity’, which tackles model uncertainty in a model-free manner. Existing similarity-based methods focus on identifying similar patterns within the series, i.e., ‘self-similarity’. In contrast, we propose searching for similar patterns from a reference set, i.e., ‘cross-similarity’. Instead of extrapolating, the future paths of the similar series are aggregated to obtain the forecasts of the target series. Building on the cross-learning concept, our approach allows the application of similarity-based forecasting on series with limited lengths. We evaluate the approach using a rich collection of real data and show that it yields competitive accuracy in both points forecasts and prediction intervals.}
}
@article{KANG2017,
title = {Visualising forecasting algorithm performance using time series instance spaces},
journal = {International Journal of Forecasting},
volume = {33},
number = {2},
pages = {345-358},
year = {2017},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2016.09.004},
url = {https://www.sciencedirect.com/science/article/pii/S0169207016301030},
author = {Yanfei Kang and Rob J. Hyndman and Kate Smith-Miles},
keywords = {M3-Competition, Time series visualisation, Time series generation, Forecasting algorithm comparison},
abstract = {It is common practice to evaluate the strength of forecasting methods using collections of well-studied time series datasets, such as the M3 data. The question is, though, how diverse and challenging are these time series, and do they enable us to study the unique strengths and weaknesses of different forecasting methods? This paper proposes a visualisation method for collections of time series that enables a time series to be represented as a point in a two-dimensional instance space. The effectiveness of different forecasting methods across this space is easy to visualise, and the diversity of the time series in an existing collection can be assessed. Noting that the diversity of the M3 dataset has been questioned, this paper also proposes a method for generating new time series with controllable characteristics in order to fill in and spread out the instance space, making our generalisations of forecasting method performances as robust as possible.}
}
@ARTICLE{kao:pf76,
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  title = {{Principal Stage of Wake Collapse in a Stratified Fluid;
          Two-Dimensional Theory}},
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}
@article{ke2017,
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  year={2017}
}
@inproceedings{Keogh2004,
author = {Keogh, Eamonn and Lonardi, Stefano and Ratanamahatana, Chotirat},
year = {2004},
month = {08},
pages = {206-215},
title = {Towards parameter-free data mining},
journal = {KDD-2004 - Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
doi = {10.1145/1014052.1014077}
}
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  address = {New York (USA)}
}
@ARTICLE{koh:arfm75,
  author = {Koh, Robert C. Y. and Brooks, Norman H.},
  year = 1975,
  title = {{Fluid Mechanics of Waste Water Disposal in the Ocean}},
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@ARTICLE{koh:jhe83,
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  year = 1983,
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@ARTICLE{koh:jhe88,
  author = {Robert C. Y. Koh},
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@article{Kohn82,
title = "When is an aggregate of a time series efficiently forecast by its past?",
journal = "Journal of Econometrics",
volume = "18",
number = "3",
pages = "337 - 349",
year = "1982",
issn = "0304-4076",
doi = "10.1016/0304-4076(82)90087-2",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/0304407682900872",
author = "Robert Kohn",
abstract = "The paper gives necessary and sufficient conditions for a linear combination of the elements of a vector time series to be efficiently forecast by its own past values, where by past we mean both the infinite and also the finite past. We observe that the problem is equivalent to testing a single element of a vector for exogeneity, this observation also suggesting a method for empirically testing the derived conditions."
}
@article{Kolassa2016,
title = "Evaluating predictive count data distributions in retail sales forecasting",
journal = "International Journal of Forecasting",
volume = "32",
number = "3",
pages = "788 - 803",
year = "2016",
issn = "0169-2070",
doi = "10.1016/j.ijforecast.2015.12.004",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0169207016000315",
author = "Stephan Kolassa",
keywords = "Demand forecasting, Density forecasting, Error measures, Intermittent demand, Proper scoring rules",
abstract = "Massive increases in computing power and new database architectures allow data to be stored and processed at finer and finer granularities, yielding count data time series with lower and lower counts. These series can no longer be dealt with using the approximative methods that are appropriate for continuous probability distributions. In addition, it is not sufficient to calculate point forecasts alone: we need to forecast entire (discrete) predictive distributions, particularly for supply chain forecasting and inventory control, but also for other planning processes. However, tools that are suitable for evaluating the quality of discrete predictive distributions are not commonly used in sales forecasting. We explore classical point forecast accuracy measures, explain why measures such as MAD, MASE and wMAPE are inherently unsuitable for count data, and use the randomized Probability Integral Transform (PIT) and proper scoring rules to compare the performances of multiple causal and noncausal forecasting models on two datasets of daily retail sales."
}
@article{Kolassa2020,
title = "Will deep and machine learning solve our forecasting problems?",
author = "S. Kolassa",
journal = "Foresight: The International Journal of Applied Forecasting",
volume = "57",
pages = "13-18",
year = "2020",
abstract = {After clarifying some terms related to ML and AI, Kolassa gives a summary of their successes in recent years, then states they are still not as good as their hype. Problems include data hunger, dubious input data, and opacity, among others. He concludes that though they are valuable additions to the tool box, they will not solve all our forecasting challenges.},
IGNOREurl = {https://EconPapers.repec.org/RePEc:for:ijafaa:y:2020:i:57:p:13-18}
}
@article{Kolmogorov1968,
author = {A. N. Kolmogorov},
title = {Three approaches to the quantitative definition of information},
journal = {International Journal of Computer Mathematics},
volume = {2},
number = {1-4},
pages = {157-168},
year  = {1968},
publisher = {Taylor \& Francis},
doi = {10.1080/00207166808803030},
URL = {https://doi.org/10.1080/00207166808803030},
eprint = {https://doi.org/10.1080/00207166808803030}
}
@article{Koning2005,
title = {{The M3 competition: Statistical tests of the results}},
journal = {International Journal of Forecasting},
volume = {21},
number = {3},
pages = {397-409},
year = {2005},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2004.10.003},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207004000810},
author = {Alex J. Koning and Philip Hans Franses and Michèle Hibon and H.O. Stekler},
keywords = {Forecasting competitions, M3 competition, Multiple comparisons, Analysis of ranks},
abstract = {The main conclusions of the M3 competition were derived from the analyses of descriptive statistics with no formal statistical testing. One of the commentaries noted that the results had not been tested for statistical significance. This paper undertakes such an analysis by examining the primary findings of that competition. We introduce a new methodology that has not previously been used to evaluate economic forecasts: multiple comparisons. We use this technique to compare each method against the best and against the mean. We conclude that the accuracy of the various methods does differ significantly, and that some methods are significantly better than others. We confirm that there is no relationship between complexity and accuracy but also show that there is a significant relationship among the various measures of accuracy. Finally, we find that the M3 conclusion that a combination of methods is better than that of the methods being combined was not proven.}
}
@PHDTHESIS{kotsovinos:phd75,
  author = {Kotsovinos, N. E.},
  year = 1975,
  title = {{A Study of the Entrainment and Turbulence in a Plane Buoyant Jet}},
  school = {California Institute of Technology, Pasadena}
}
@article{KOURENTZES2013198,
title = {Intermittent demand forecasts with neural networks},
journal = {International Journal of Production Economics},
volume = {143},
number = {1},
pages = {198-206},
year = {2013},
issn = {0925-5273},
doi = {https://doi.org/10.1016/j.ijpe.2013.01.009},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0925527313000273},
author = {Nikolaos Kourentzes},
keywords = {Intermittent demand, Neural networks, Croston's method, Forecasting, Slow moving items},
abstract = {Intermittent demand appears when demand events occur only sporadically. Typically such time series have few observations making intermittent demand forecasting challenging. Forecast errors can be costly in terms of unmet demand or obsolescent stock. Intermittent demand forecasting has been addressed using established forecasting methods, including simple moving averages, exponential smoothing and Croston's method with its variants. This study proposes a neural network (NN) methodology to forecast intermittent time series. These NNs are used to provide dynamic demand rate forecasts, which do not assume constant demand rate in the future and can capture interactions between the non-zero demand and the inter-arrival rate of demand events. This overcomes the limitations of Croston's method. In order to mitigate the issue of limited fitting sample, which is common in intermittent demand, the proposed models use regularised training and median ensembles over multiple training initialisations to produce robust forecasts. The NNs are evaluated against established benchmarks using both forecasting accuracy and inventory metrics. The findings of forecasting and inventory metrics are conflicting. While NNs achieved poor forecasting accuracy and bias, all NN variants achieved higher service levels than the best performing Croston's method variant, without requiring analogous increases in stock holding volume. Therefore, NNs are found to be effective for intermittent demand applications. This study provides further arguments and evidence against the use of conventional forecasting accuracy metrics to evaluate forecasting methods for intermittent demand, concluding that attention to inventory metrics is desirable.}
}
@article{KOURENTZES2014180,
title = {On intermittent demand model optimisation and selection},
journal = {International Journal of Production Economics},
volume = {156},
pages = {180-190},
year = {2014},
issn = {0925-5273},
doi = {https://doi.org/10.1016/j.ijpe.2014.06.007},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S092552731400190X},
author = {Nikolaos Kourentzes},
keywords = {Intermittent demand, Croston׳s method, SBA method, TSB method, Forecasting, Optimisation, Model selection},
abstract = {Intermittent demand time series involve items that are requested infrequently, resulting in sporadic demand. Croston׳s method and its variants have been proposed in the literature to address this forecasting problem. Recently other novel methods have appeared. Although the literature provides guidance on the suggested range for model parameters, a consistent and valid optimisation methodology is lacking. Growing evidence in the literature points against the use of conventional accuracy error metrics for model evaluation for intermittent demand time series. Consequently these may be inappropriate for parameter or model selection. This paper contributes to the discussion by evaluating a series of conventional time series error metrics, along with two novel ones for parameter optimisation for intermittent demand methods. The proposed metrics are found to not only perform best, but also provide consistent parameters with the literature, in contrast to conventional metrics. Furthermore, this work validates that employing different parameters for smoothing the non-zero demand and the inter-demand intervals of Croston׳s method and its variants is beneficial. The evaluated error metrics are considered for automatic model selection for each time series. Although they are found to perform similar to theory driven model selection schemes, they fail to outperform single models substantially. These findings are validated using both out-of-sample forecast evaluation and inventory simulations.}
}
@article{Kourentzes14,
  author = {Nikolaos Kourentzes and Fotios Petropoulos and Juan R. Trapero},
  title = {Improving forecasting by estimating time series structural components across multiple frequencies},
  journal = {International Journal of Forecasting},
  volume = {30},
  number = {2},
  pages = {291-302},
  year = {2014},
  doi = {10.1016/j.ijforecast.2013.09.006}
}
@article{Kourentzes16,
title = {Forecasting with multivariate temporal aggregation: The case of promotional modelling},
journal = {International Journal of Production Economics},
volume = {181},
pages = {145-153},
year = {2016},
note = {SI: ISIR 2014},
issn = {0925-5273},
doi = {10.1016/j.ijpe.2015.09.011},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0925527315003382},
author = {Nikolaos Kourentzes and Fotios Petropoulos},
keywords = {Forecasting, Temporal aggregation, MAPA, Exponential smoothing, Promotional modelling},
abstract = {Demand forecasting is central to decision making and operations in organisations. As the volume of forecasts increases, for example due to an increased product customisation that leads to more SKUs being traded, or a reduction in the length of the forecasting cycle, there is a pressing need for reliable automated forecasting. Conventionally, companies rely on a statistical baseline forecast that captures only past demand patterns, which is subsequently adjusted by human experts to incorporate additional information such as promotions. Although there is evidence that such process adds value to forecasting, it is questionable how much it can scale up, due to the human element. Instead, in the literature it has been proposed to enhance the baseline forecasts with external well-structured information, such as the promotional plan of the company, and let experts focus on the less structured information, thus reducing their workload and allowing them to focus where they can add most value. This change in forecasting support systems requires reliable multivariate forecasting models that can be automated, accurate and robust. This paper proposes an extension of the recently proposed Multiple Aggregation Prediction Algorithm (MAPA), which uses temporal aggregation to improve upon the established exponential smoothing family of methods. MAPA is attractive as it has been found to increase both the accuracy and robustness of exponential smoothing. The extended multivariate MAPA is evaluated against established benchmarks in modelling a number of heavily promoted products and is found to perform well in terms of forecast bias and accuracy. Furthermore, we demonstrate that modelling time series using multiple temporal aggregation levels makes the final forecast robust to model mis-specification.}
}
@article{Kourentzes2017,
title = {Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels?},
journal = {Journal of Business Research},
volume = {78},
pages = {1-9},
year = {2017},
issn = {0148-2963},
doi = {10.1016/j.jbusres.2017.04.016},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0148296317301376},
author = {Nikolaos Kourentzes and Bahman Rostami-Tabar and Devon K. Barrow}
}
@article{Kourentzes19a,
author = "Nikolaos Kourentzes and George Athanasopoulos",
title = "Cross-temporal coherent forecasts for Australian tourism",
journal = "Annals of Tourism Research",
volume = "75",
pages = "393 - 409",
year = "2019",
issn = "0160-7383",
doi = "10.1016/j.annals.2019.02.001",
note = {doi:10.1016/j.annals.2019.02.001}
}
@article{Kourentzes2019b,
title = {Another look at forecast selection and combination: Evidence from forecast pooling},
journal = {International Journal of Production Economics},
volume = {209},
pages = {226-235},
year = {2019},
IGNOREnote = {The Proceedings of the 19th International Symposium on Inventories},
issn = {0925-5273},
doi = {10.1016/j.ijpe.2018.05.019},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0925527318302196},
author = {Nikolaos Kourentzes and Devon Barrow and Fotios Petropoulos}
}
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author = "R.J. Kuo"
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year = {2017},
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}
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year = {2013},
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IGNOREurl = {},
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title = {Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category},
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year = {2020},
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IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698919304011},
author = {Kunlin Li and Yuhan Chen and Liyi Zhang}
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title = {Why do some combinations perform better than others?},
journal = {International Journal of Forecasting},
volume = {36},
number = {1},
pages = {142-149},
year = {2020},
note = {M4 Competition},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2019.03.027},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207019301475},
author = {Kenneth C. Lichtendahl and Robert L. Winkler}
}
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}
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publisher = {Taylor & Francis},
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note = {doi:10.1080/07350015.1984.10509388},
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}
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title = "Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information",
journal = "European Journal of Operational Research",
volume = "249",
number = "1",
pages = "245-257",
year = "2016",
issn = "0377-2217",
doi = "10.1016/j.ejor.2015.08.029",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0377221715007845",
author = "Shaohui Ma and Robert Fildes and Tao Huang",
keywords = "Analytics, OR in marketing, Forecasting, Retailing, Promotions",
abstract = "In marketing analytics applications in OR, the modeler often faces the problem of selecting key variables from a large number of possibilities. For example, SKU level retail store sales are affected by inter and intra category effects which potentially need to be considered when deciding on promotional strategy and producing operational forecasts. But no research has yet put this well accepted concept into forecasting practice: an obvious obstacle is the ultra-high dimensionality of the variable space. This paper develops a four steps methodological framework to overcome the problem. It is illustrated by investigating the value of both intra- and inter-category SKU level promotional information in improving forecast accuracy. The method consists of the identification of potentially influential categories, the building of the explanatory variable space, variable selection and model estimation by a multistage LASSO regression, and the use of a rolling scheme to generate forecasts. The success of this new method for dealing with high dimensionality is demonstrated by improvements in forecasting accuracy compared to alternative methods of simplifying the variable space. The empirical results show that models integrating more information perform significantly better than the baseline model when using the proposed methodology framework. In general, we can improve the forecasting accuracy by 12.6 percent over the model using only the SKU's own predictors. But of the improvements achieved, 95 percent of it comes from the intra-category information, and only 5 percent from the inter-category information. The substantive marketing results also have implications for promotional category management."
}
@article{Ma2017,
title = {{A retail store SKU promotions optimization model for category multi-period profit maximization}},
journal = "European Journal of Operational Research",
volume = "260",
number = "2",
pages = "680-692",
year = "2017",
issn = "0377-2217",
doi = "10.1016/j.ejor.2016.12.032",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0377221716310669",
author = "Shaohui Ma and Robert Fildes",
keywords = "OR in marketing, Promotion optimization, Demand forecasting, Fast-moving consumer goods retailing",
abstract = "Consumer promotions are an important element of competitive dynamics in retail markets and make a significant difference in the retailer's profits. But no study has so far included all the elements that are required to meet retail business objectives. We extend the existing literatures by considering all the basic requirements for a promotional Decision Support System (DSS): reliance on operational (store-level) data only, the ability to predict sales as a function of prices and the inclusion of other promotional variables affecting the category. The new model delivers an optimizing promotional schedule at Stock-Keeping-Unit (SKU) level which maximizes multi-period category level profit under the constraints of business rules typically applied in practice. We first develop a high dimensional distributed lag demand model which integrates both cross-SKU competitive promotion information and cross-period promotional influences. We estimate the model by proposing a two stage sign constrained regularization approach to ensure realistic promotional parameters. Based on the demand model, we then build a nonlinear integer programming model to maximize the retailer's category profits over a planning horizon under constraints that model important business rules. The output of the model provides optimized prices, display and feature advertising planning together with sales and profit forecasts. Empirical tests over a number of stores and categories using supermarket data suggest that our model generates accurate sales forecasts and increases category profits by approximately 17\% and that including cross-item and cross-period effects is also valuable."
}
@article{Ma2021,
title = {Retail sales forecasting with meta-learning},
journal = {European Journal of Operational Research},
volume = {288},
number = {1},
pages = {111-128},
year = {2021},
issn = {0377-2217},
doi = {10.1016/j.ejor.2020.05.038},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0377221720304847},
author = {Shaohui Ma and Robert Fildes},
keywords = {Forecasting, Big data, Retail sales forecasting, Machine learning, Forecasting many time series, Meta-learning, Deep learning},
abstract = {Retail sales forecasting often requires forecasts for thousands of products for many stores. We present a meta-learning framework based on newly developed deep convolutional neural networks, which can first learn a feature representation from raw sales time series data automatically, and then link the learnt features with a set of weights which are used to combine a pool of base-forecasting methods. The experiments which are based on IRI weekly data show that the proposed meta-learner provides superior forecasting performance compared with a number of state-of-art benchmarks, though the accuracy gains over some more sophisticated meta ensemble benchmarks are modest and the learnt features lack interpretability. When designing a meta-learner in forecasting retail sales, we recommend building a pool of base-forecasters including both individual and pooled forecasting methods, and target finding the best combination forecasts instead of the best individual method.}
}
@BOOK{Makridakis98,
  author = {S. Makridakis and S. Wheelwright and Rob J Hyndman},
  year = 1998,
  title = {{Forecasting: Methods and Applications, 3rd Edition}},
  publisher = {{John Wiley \& Sons, New York}}
}
@article{Makridakis2000,
title = {The M3-Competition: results, conclusions and implications},
journal = {International Journal of Forecasting},
volume = {16},
number = {4},
pages = {451-476},
year = {2000},
note = {The M3- Competition},
issn = {0169-2070},
doi = {10.1016/S0169-2070(00)00057-1},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207000000571},
author = {Spyros Makridakis and Michèle Hibon}
}
@article{Makridakis2018,
title = {Statistical and Machine Learning forecasting methods: Concerns and ways forward},
journal = {PLoS ONE},
volume = {13},
number = {3},
pages = {1-26},
year = {2018},
issn = {0169-2070},
note = {e0194889},
doi = {10.1371/journal.pone.0194889},
author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos}
}
@article{Makridakis2020,
title = {{The {M4} Competition: 100,000 time series and 61 forecasting methods}},
journal = {International Journal of Forecasting},
volume = {36},
number = {1},
pages = {54-74},
year = {2020},
note = {{M4} Competition},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2019.04.014},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207019301128},
author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos}
}
@article{Makridakis2021,
title = {The {M5} competition: Background, organization, and implementation},
journal = {International Journal of Forecasting},
year = {2022},
volume = {38},
pages = {1325-1336},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2021.07.007},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207021001187},
author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos}
}
@article{Makridakis2022a,
title = {{M5} accuracy competition: Results, findings, and conclusions},
journal = {International Journal of Forecasting},
year = {2022},
volume = {38},
pages = {1346-1364},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2021.11.013},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207021001874},
author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos}
}
@article{Makridakis2022b,
title = {The {M5} uncertainty competition: Results, findings and conclusions},
journal = {International Journal of Forecasting},
year = {2022},
volume = {38},
pages = {1365-1385},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2021.10.009},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207021001722},
author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos and Zhi Chen and Anil Gaba and Ilia Tsetlin and Robert L. Winkler}
}
@article{Makridakis2022c,
author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos and Artemios-Anargyros Semenoglou and Gary Mulder and Konstantinos Nikolopoulos},
title = {Statistical, machine learning and deep learning forecasting methods: Comparisons and ways forward},
journal = {Journal of the Operational Research Society},
volume = {0},
number = {0},
pages = {1-20},
year  = {2022},
publisher = {Taylor & Francis},
doi = {10.1080/01605682.2022.2118629},
IGNOREURL = {10.1080/01605682.2022.2118629},
IGNOREeprint = { 10.1080/01605682.2022.2118629}
}
@article{Makridakis2022d,
title = {Predicting/hypothesizing the findings of the {M5} competition},
journal = {International Journal of Forecasting},
volume = {38},
number = {4},
pages = {1337-1345},
year = {2022},
note = {Special Issue: M5 competition},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2021.09.014},
IGNOREURL = {https://www.sciencedirect.com/science/article/pii/S0169207021001631},
author = {Spyros Makridakis and Evangelos Spiliotis and Vassilios Assimakopoulos},
keywords = {Forecasting competition, M competition, Accuracy, Uncertainty, Retail sales forecasting},
abstract = {The scientific method consists of making hypotheses or predictions and then carrying out experiments to test them once the actual results have become available, in order to learn from both successes and mistakes. This approach was followed in the M4 competition with positive results and has been repeated in the M5, with its organizers submitting their ten predictions/hypotheses about its expected results five days before its launch. The present paper presents these predictions/hypotheses and evaluates their realization according to the actual findings of the competition. The results indicate that well-established practices, like combining forecasts, exploiting explanatory variables, and capturing seasonality and special days, remain critical for enhancing forecasting performance, re-confirming also that relatively new approaches, like cross-learning algorithms and machine learning methods, display great potential. Yet, we show that simple, local statistical methods may still be competitive for forecasting high granularity data and estimating the tails of the uncertainty distribution, thus motivating future research in the field of retail sales forecasting.}
}
@ARTICLE{mancini:jwpc78,
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@article{MANCUSO2021,
title = {A machine learning approach for forecasting hierarchical time series},
journal = {Expert Systems with Applications},
volume = {182},
pages = {115102},
year = {2021},
issn = {0957-4174},
doi = {10.1016/j.eswa.2021.115102},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0957417421005431},
author = {Paolo Mancuso and Veronica Piccialli and Antonio M. Sudoso},
keywords = {Hierarchical time series, Forecast, Machine learning, Deep neural network},
abstract = {In this paper, we propose a machine learning approach for forecasting hierarchical time series. When dealing with hierarchical time series, apart from generating accurate forecasts, one needs to select a suitable method for producing reconciled forecasts. Forecast reconciliation is the process of adjusting forecasts to make them coherent across the hierarchy. In literature, coherence is often enforced by using a post-processing technique on the base forecasts produced by suitable time series forecasting methods. On the contrary, our idea is to use a deep neural network to directly produce accurate and reconciled forecasts. We exploit the ability of a deep neural network to extract information capturing the structure of the hierarchy. We impose the reconciliation at training time by minimizing a customized loss function. In many practical applications, besides time series data, hierarchical time series include explanatory variables that are beneficial for increasing the forecasting accuracy. Exploiting this further information, our approach links the relationship between time series features extracted at any level of the hierarchy and the explanatory variables into an end-to-end neural network providing accurate and reconciled point forecasts. The effectiveness of the approach is validated on three real-world datasets, where our method outperforms state-of-the-art competitors in hierarchical forecasting.}
}
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author = "Shelby H. McIntyre and Dale D. Achabal and Christopher M. Miller"
}
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year = "2014",
note = "Sustainable Food Supply Chain Management",
issn = "0925-5273",
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AUTHOR = {Monteiro-Santos, João and Gonçalves, Hernâni and Bernardes, João and Antunes, Luís and Nozari, Mohammad and Costa-Santos, Cristina},
TITLE = {Entropy and Compression Capture Different Complexity Features: The Case of Fetal Heart Rate},
JOURNAL = {Entropy},
VOLUME = {19},
YEAR = {2017},
NUMBER = {12},
ARTICLE-NUMBER = {688},
URL = {https://www.mdpi.com/1099-4300/19/12/688},
ISSN = {1099-4300},
ABSTRACT = {Entropy and compression have been used to distinguish fetuses at risk of hypoxia from their healthy counterparts through the analysis of Fetal Heart Rate (FHR). Low correlation that was observed between these two approaches suggests that they capture different complexity features. This study aims at characterizing the complexity of FHR features captured by entropy and compression, using as reference international guidelines. Single and multi-scale approaches were considered in the computation of entropy and compression. The following physiologic-based features were considered: FHR baseline; percentage of abnormal long (\%abLTV) and short (\%abSTV) term variability; average short term variability; and, number of acceleration and decelerations. All of the features were computed on a set of 68 intrapartum FHR tracings, divided as normal, mildly, and moderately-severely acidemic born fetuses. The correlation between entropy/compression features and the physiologic-based features was assessed. There were correlations between compressions and accelerations and decelerations, but neither accelerations nor decelerations were significantly correlated with entropies. The \%abSTV was significantly correlated with entropies (ranging between −0.54 and −0.62), and to a higher extent with compression (ranging between −0.80 and −0.94). Distinction between groups was clearer in the lower scales using entropy and in the higher scales using compression. Entropy and compression are complementary complexity measures.},
DOI = {10.3390/e19120688}
}
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pages = {86-92},
year = {2020},
note = {M4 Competition},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2019.02.011},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207019300895},
author = {Pablo Montero-Manso and George Athanasopoulos and Rob J. Hyndman and Thiyanga S. Talagala}
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author = {Pablo Montero-Manso and Rob J. Hyndman}
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pages = {322-329},
year = {2018},
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author = {Konstantinos Nikolopoulos and Fotios Petropoulos}
}
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      primaryClass={stat.ML}
}
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@Article{Oliveira2019,
AUTHOR = {Oliveira, José Manuel and Ramos, Patrícia},
TITLE = {Assessing the Performance of Hierarchical Forecasting Methods on the Retail Sector},
JOURNAL = {Entropy},
VOLUME = {21},
YEAR = {2019},
NUMBER = {4},
ARTICLE-NUMBER = {436},
IGNOREurl = {https://www.mdpi.com/1099-4300/21/4/436},
ISSN = {1099-4300},
ABSTRACT = {Retailers need demand forecasts at different levels of aggregation in order to support a variety of decisions along the supply chain. To ensure aligned decision-making across the hierarchy, it is essential that forecasts at the most disaggregated level add up to forecasts at the aggregate levels above. It is not clear if these aggregate forecasts should be generated independently or by using an hierarchical forecasting method that ensures coherent decision-making at the different levels but does not guarantee, at least, the same accuracy. To give guidelines on this issue, our empirical study investigates the relative performance of independent and reconciled forecasting approaches, using real data from a Portuguese retailer. We consider two alternative forecasting model families for generating the base forecasts; namely, state space models and ARIMA. Appropriate models from both families are chosen for each time-series by minimising the bias-corrected Akaike information criteria. The results show significant improvements in forecast accuracy, providing valuable information to support management decisions. It is clear that reconciled forecasts using the Minimum Trace Shrinkage estimator (MinT-Shrink) generally improve on the accuracy of the ARIMA base forecasts for all levels and for the complete hierarchy, across all forecast horizons. The accuracy gains generally increase with the horizon, varying between 1.7\% and 3.7\% for the complete hierarchy. It is also evident that the gains in forecast accuracy are more substantial at the higher levels of aggregation, which means that the information about the individual dynamics of the series, which was lost due to aggregation, is brought back again from the lower levels of aggregation to the higher levels by the reconciliation process, substantially improving the forecast accuracy over the base forecasts.},
DOI = {10.3390/e21040436}
}
@InProceedings{oliveira2023EANN,
author="Oliveira, Jos{\'e} Manuel
and Ramos, Patr{\'i}cia",
editor="Iliadis, Lazaros
and Maglogiannis, Ilias
and Alonso, Serafin
and Jayne, Chrisina
and Pimenidis, Elias",
title="Cross-Learning-Based Sales Forecasting Using Deep Learning via Partial Pooling from Multi-level Data",
booktitle="Engineering Applications of Neural Networks",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="279--290",
abstract="Sales forecasts are an important tool for inventory management, allowing retailers to balance inventory levels with customer demand and market conditions. By using sales forecasts to inform inventory management decisions, companies can optimize their inventory levels and avoid costly stockouts or excess inventory costs. The scale of the forecasting problem in the retail domain is significant and requires ongoing attention and resources to ensure accurate and effective forecasting. Recent advances in machine learning algorithms such as deep learning have made possible to build more sophisticated forecasting models that can learn from large amounts of data. These global models can capture complex patterns and relationships in the data and predict demand across multiple regions and product categories. In this paper, we investigate the cross-learning scenarios, inspired by the product hierarchy frequently utilized in retail planning, which enable global models to better capture interdependencies between different products and regions. Our empirical results obtained using M5 competition dataset indicate that the cross-learning approaches exhibit significantly superior performance compared to local forecasting benchmarks. Our findings also suggest that using partial pooling at the lowest aggregation level of the retail hierarchical allows for a more effective capture of the distinct characteristics of each group.",
isbn="978-3-031-34204-2",
doi={10.1007/978-3-031-34204-2_24}
}
@Article{Oliveira2023BDCC,
AUTHOR = {Oliveira, José Manuel and Ramos, Patrícia},
TITLE = {Investigating the Accuracy of Autoregressive Recurrent Networks Using Hierarchical Aggregation Structure-Based Data Partitioning},
JOURNAL = {Big Data and Cognitive Computing},
VOLUME = {7},
YEAR = {2023},
NUMBER = {2},
ARTICLE-NUMBER = {100},
URL = {https://www.mdpi.com/2504-2289/7/2/100},
ISSN = {2504-2289},
ABSTRACT = {Global models have been developed to tackle the challenge of forecasting sets of series that are related or share similarities, but they have not been developed for heterogeneous datasets. Various methods of partitioning by relatedness have been introduced to enhance the similarities of sets, resulting in improved forecasting accuracy but often at the cost of a reduced sample size, which could be harmful. To shed light on how the relatedness between series impacts the effectiveness of global models in real-world demand-forecasting problems, we perform an extensive empirical study using the M5 competition dataset. We examine cross-learning scenarios driven by the product hierarchy commonly employed in retail planning to allow global models to capture interdependencies across products and regions more effectively. Our findings show that global models outperform state-of-the-art local benchmarks by a considerable margin, indicating that they are not inherently more limited than local models and can handle unrelated time-series data effectively. The accuracy of data-partitioning approaches increases as the sizes of the data pools and the models&rsquo; complexity decrease. However, there is a trade-off between data availability and data relatedness. Smaller data pools lead to increased similarity among time series, making it easier to capture cross-product and cross-region dependencies, but this comes at the cost of a reduced sample, which may not be beneficial. Finally, it is worth noting that the successful implementation of global models for heterogeneous datasets can significantly impact forecasting practice.},
DOI = {10.3390/bdcc7020100}
}
@inproceedings{optuna2019,
author = {Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
title = {Optuna: A Next-Generation Hyperparameter Optimization Framework},
year = {2019},
isbn = {9781450362016},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3292500.3330701},
doi = {10.1145/3292500.3330701},
abstract = {The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).},
booktitle = {25th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages = {2623–2631},
numpages = {9},
keywords = {hyperparameter optimization, machine learning system, black-box optimization, Bayesian optimization},
location = {Anchorage, AK, USA},
series = {KDD '19}
}
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ISSN = {00028282},
IGNOREURL = {http://www.jstor.org/stable/1815532},
author = {Guy H. Orcutt and Harold W. Watts and John B. Edwards},
journal = {The American Economic Review},
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@book{Ord2017,
      title     = "Principles of business forecasting (2nd ed.)",
      author    = "J. K. Ord and R. Fildes and N. Kourentzes",
      year      = 2017,
      publisher = "Wessex Press Publishing Co",
      address   = "London"
    }
@misc{oreshkin2020nbeats,
      title={N-BEATS: Neural basis expansion analysis for interpretable time series forecasting}, 
      author={Boris N. Oreshkin and Dmitri Carpov and Nicolas Chapados and Yoshua Bengio},
      year={2020},
      eprint={1905.10437},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}    
@article{Ouyang2007,
title = "The effect of information sharing on supply chain stability and the bullwhip effect",
journal = "European Journal of Operational Research",
volume = "182",
number = "3",
pages = "1107 - 1121",
year = "2007",
issn = "0377-2217",
doi = "10.1016/j.ejor.2006.09.037",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S037722170600960X",
author = "Yanfeng Ouyang"
}
@INPROCEEDINGS{Pan13,
  author = {Y. Pan and T. Pohlen and S. Manago},
  year = 2013,
  title = {{Hybrid neural network model in forecasting aggregate U.S. retail sales}},
  booktitle = {Advances in Business and Management Forecasting, Volume 9},
  pages = {153-170},
  publisher = {Emerald Group Publishing Limited},
  editor = {Kenneth D. Lawrence and Ronald K. Klimberg}
}
@article{Panagiotelis2019,
title = {{Macroeconomic forecasting for Australia using a large number of predictors}},
journal = {International Journal of Forecasting},
volume = {35},
number = {2},
pages = {616-633},
year = {2019},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2018.12.002},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207019300056},
author = {Anastasios Panagiotelis and George Athanasopoulos and Rob J. Hyndman and Bin Jiang and Farshid Vahid},
keywords = {Bayesian VAR, Bagging, Dynamic factor model, Ridge regression, Least angular regression, Shrinkage, Regularization},
abstract = {A popular approach to forecasting macroeconomic variables is to utilize a large number of predictors. Several regularization and shrinkage methods can be used to exploit such high-dimensional datasets, and have been shown to improve forecast accuracy for the US economy. To assess whether similar results hold for economies with different characteristics, an Australian dataset containing observations on 151 aggregate and disaggregate economic series as well as 185 international variables, is introduced. An extensive empirical study is carried out investigating forecasts at different horizons, using a variety of methods and with information sets containing an increasing number of predictors. In contrast to other countries the results show that it is difficult to forecast Australian key macroeconomic variables more accurately than some simple benchmarks. In line with other studies we also find that there is little to no improvement in forecast accuracy when the number of predictors is expanded beyond 20–40 variables and international factors do not seem to help.}
}
@unpublished{Panagiotelis2020,
author = {Anastasios Panagiotelis and Puwasala Gamakumara and George Athanasopoulos and Rob J Hyndman},
file = {:Users/Desktop/Mendeley Papers/SSRN-id2731096.pdf:pdf},
institution = {Monash University, Australia},
title = {{Probabilistic forecast reconciliation: properties, evaluation and score optimisation}},
year = {2020}
}
@article{Pantano2020,
title = {Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis},
journal = {Journal of Retailing and Consumer Services},
volume = {55},
pages = {102096},
year = {2020},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2020.102096},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698919311865},
author = {Eleonora Pantano and Gabriele Pizzi},
keywords = {Online customer assistance, Artificial intelligence (AI), Chatbot, Patent analysis, Online retailing, Conversational agents},
abstract = {The main goal of this research is to provide a comprehensive understanding of the actual progresses in artificial intelligence, with emphasis on chatbots as emerging forms of customer assistance in online retailing. Drawing upon an analysis of the chatbot patents in the past 20 years, our findings show the increasing technology push towards the adoption of new conversational agents based on natural language. Findings also highlight the extent to which the research and development efforts are attempting to improve artificial intelligence systems that characterize chatbots. To this end, technology advancements are mainly focusing on: (i) improving chatbot ability to automatically draw inferences on users starting from multiple data sources, and (ii) using consumers’ knowledge adaptively to provide more customized solutions. Finally, results show the tight relationship between the digital assistants’ analytical skills and their ability to automatically interact with the users.}
}
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number = {4},
pages = {807--831},
year = {2018},
note = {doi:10.1007/s11600-018-0120-7},
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}
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author = {G. Papacharalampous and H. Tyralis and D. Koutsoyiannis},
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volume = {5},
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year = {2018},
note = {doi:10.1186/s40562-018-0111-1},
doi = {10.1186/s40562-018-0111-1}
}
@article{Papacharalampous19,
author = {G. Papacharalampous and H. Tyralis and D. Koutsoyiannis},
title = {Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes},
journal = {Stochastic Environmental Research and Risk Assessment},
year = {2019},
note = {doi:10.1007/s00477-018-1638-6},
doi = {10.1007/s00477-018-1638-6}
}
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@article{Pennings17,
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doi = "10.1016/j.ejor.2017.04.047",
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author = "Clint L.P. Pennings and Jan van Dalen",
note = {doi:10.1016/j.ejor.2017.04.047}
}
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  year = 1996,
  title = {{Comparison of Near-Field Dilutions from In Situ Measurements and Simulated Dilutions at the Sand Island, HI Sewage Outfall Plume}},
  booktitle = {Proceedings of the North American Water and Environment
              Congress '96},
  address = {Anaheim}
}
@ARTICLE{petrenko:jgr97,
  author = {Petrenko, A. A. and Jones, B. H. and Dickey, T. D. and LeHaitre, M. and Moore, C.},
  year = 1997,
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}
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  editor = {ASCE},
  month = {June},
  year = 1998,
  title = {{Shape and Initial Dilution of Sand Island, Hawaii Sewage Plume}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 124,
  number = 6,
  pages = {565-571}
}
@PHDTHESIS{petrenko:phd97,
  author = {Anne Alexandra Petrenko},
  year = 1997,
  title = {{Detection and Characterization of the Sewage Plume at Sand Island, Hawaii}},
  school = {Faculty of the Graduate School, University of California}
}
@article{Petropoulos14,
title = "‘Horses for Courses’ in demand forecasting",
journal = "European Journal of Operational Research",
volume = "237",
number = "1",
pages = "152 - 163",
year = "2014",
issn = "0377-2217",
doi = "10.1016/j.ejor.2014.02.036",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0377221714001714",
author = "Fotios Petropoulos and Spyros Makridakis and Vassilios Assimakopoulos and Konstantinos Nikolopoulos"
}
@article{Petropoulos2020,
title = {A simple combination of univariate models},
journal = {International Journal of Forecasting},
volume = {36},
number = {1},
pages = {110-115},
year = {2020},
note = {M4 Competition},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2019.01.006},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207019300585},
author = {Fotios Petropoulos and Ivan Svetunkov}
}
@article{PETROPOULOS2022705,
title = {Forecasting: theory and practice},
journal = {International Journal of Forecasting},
volume = {38},
number = {3},
pages = {705-871},
year = {2022},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2021.11.001},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207021001758},
author = {Fotios Petropoulos and Daniele Apiletti and Vassilios Assimakopoulos and Mohamed Zied Babai and Devon K. Barrow and Souhaib {Ben Taieb} and Christoph Bergmeir and Ricardo J. Bessa and Jakub Bijak and John E. Boylan and Jethro Browell and Claudio Carnevale and Jennifer L. Castle and Pasquale Cirillo and Michael P. Clements and Clara Cordeiro and Fernando Luiz {Cyrino Oliveira} and Shari {De Baets} and Alexander Dokumentov and Joanne Ellison and Piotr Fiszeder and Philip Hans Franses and David T. Frazier and Michael Gilliland and M. Sinan Gönül and Paul Goodwin and Luigi Grossi and Yael Grushka-Cockayne and Mariangela Guidolin and Massimo Guidolin and Ulrich Gunter and Xiaojia Guo and Renato Guseo and Nigel Harvey and David F. Hendry and Ross Hollyman and Tim Januschowski and Jooyoung Jeon and Victor Richmond R. Jose and Yanfei Kang and Anne B. Koehler and Stephan Kolassa and Nikolaos Kourentzes and Sonia Leva and Feng Li and Konstantia Litsiou and Spyros Makridakis and Gael M. Martin and Andrew B. Martinez and Sheik Meeran and Theodore Modis and Konstantinos Nikolopoulos and Dilek Önkal and Alessia Paccagnini and Anastasios Panagiotelis and Ioannis Panapakidis and Jose M. Pavía and Manuela Pedio and Diego J. Pedregal and Pierre Pinson and Patrícia Ramos and David E. Rapach and J. James Reade and Bahman Rostami-Tabar and Michał Rubaszek and Georgios Sermpinis and Han Lin Shang and Evangelos Spiliotis and Aris A. Syntetos and Priyanga Dilini Talagala and Thiyanga S. Talagala and Len Tashman and Dimitrios Thomakos and Thordis Thorarinsdottir and Ezio Todini and Juan Ramón {Trapero Arenas} and Xiaoqian Wang and Robert L. Winkler and Alisa Yusupova and Florian Ziel},
keywords = {Review, Encyclopedia, Methods, Applications, Principles, Time series, Prediction},
abstract = {Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases.}
}
@Article{Pina2021,
AUTHOR = {Pina, Miguel and Gaspar, Pedro Dinis and Lima, Tânia Miranda},
TITLE = {Decision Support System in Dynamic Pricing of Horticultural Products Based on the Quality Decline Due to Bacterial Growth},
JOURNAL = {Applied System Innovation},
VOLUME = {4},
YEAR = {2021},
NUMBER = {4},
ARTICLE-NUMBER = {80},
URL = {https://www.mdpi.com/2571-5577/4/4/80},
ISSN = {2571-5577},
DOI = {10.3390/asi4040080}
}
@inproceedings{Pinho2016AMT,
Author={Pinho, JM and Oliveira, JM and Ramos, P},
Title={{Sales Forecasting in Retail Industry Based on Dynamic Regression Models}},
Year={{2016}},
Series={{Advances in Transdisciplinary Engineering}},
Editor={Goh, Ym and Case, K},
Booktitle={{Advances in Manufacturing Technology XXX}},
Volume={{3}},
Pages={{483-488}},
DOI={10.3233/978-1-61499-668-2-483}, 
Type={Proceedings Paper}
}
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  author = {D. J. Baumgartner and W. E. Frick and P. J. W. Roberts},
  month = {March},
  year = 1994,
  title = {{Dilution Models for Effluent Discharges}},
  note = {Rep. No. EPA/600/R-94/086},
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}

@InProceedings{pmlr-v139-rangapuram21a,
  title = 	 {End-to-End Learning of Coherent Probabilistic Forecasts for Hierarchical Time Series},
  author =       {Rangapuram, Syama Sundar and Werner, Lucien D and Benidis, Konstantinos and Mercado, Pedro and Gasthaus, Jan and Januschowski, Tim},
  booktitle = 	 {38th International Conference on Machine Learning},
  pages = 	 {8832--8843},
  year = 	 {2021},
  editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  series = 	 {Machine Learning Research},
  month = 	 {18--24 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v139/rangapuram21a/rangapuram21a.pdf},
  url = 	 {https://proceedings.mlr.press/v139/rangapuram21a.html},
  abstract = 	 {This paper presents a novel approach for hierarchical time series forecasting that produces coherent, probabilistic forecasts without requiring any explicit post-processing reconciliation. Unlike the state-of-the-art, the proposed method simultaneously learns from all time series in the hierarchy and incorporates the reconciliation step into a single trainable model. This is achieved by applying the reparameterization trick and casting reconciliation as an optimization problem with a closed-form solution. These model features make end-to-end learning of hierarchical forecasts possible, while accomplishing the challenging task of generating forecasts that are both probabilistic and coherent. Importantly, our approach also accommodates general aggregation constraints including grouped and temporal hierarchies. An extensive empirical evaluation on real-world hierarchical datasets demonstrates the advantages of the proposed approach over the state-of-the-art.}
}
@InProceedings{pmlr-v206-rangapuram23a,
  title = 	 {Coherent Probabilistic Forecasting of Temporal Hierarchies},
  author =       {Rangapuram, Syama Sundar and Kapoor, Shubham and Nirwan, Rajbir Singh and Mercado, Pedro and Januschowski, Tim and Wang, Yuyang and Bohlke-Schneider, Michael},
  booktitle = 	 {26th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {9362--9376},
  year = 	 {2023},
  editor = 	 {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
  volume = 	 {206},
  series = 	 {Machine Learning Research},
  month = 	 {25--27 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v206/rangapuram23a/rangapuram23a.pdf},
  url = 	 {https://proceedings.mlr.press/v206/rangapuram23a.html},
  abstract = 	 {Forecasts at different time granularities are required in practice for addressing various business problems starting from short-term operational to medium-term tactical and to long-term strategic planning. These forecasting problems are usually treated independently by learning different ML models which results in forecasts that are not consistent with the temporal aggregation structure, leading to inefficient decision making. Some of the recent work addressed this problem, however, it uses a post-hoc reconciliation strategy, which results in sub-optimal results and cannot produce probabilistic forecasts. In this paper, we present a global model that produces coherent, probabilistic forecasts for different time granularities by learning joint embeddings for the different aggregation levels with graph neural networks and temporal reconciliation. Temporal reconciliation not only enables consistent decisions for business problems across different planning horizons but also improves the quality of forecasts at finer time granularities. A thorough empirical evaluation illustrates the benefits of the proposed method.}
}
@BOOK{potter:MechanicsFluids97,
  author = {Merle C. Potter and David C. Wiggert and Midhat Hondzo},
  year = 1997,
  title = {{Mechanics of Fluids, Second Edition}},
  note = {{ISBN - 0132076225}},
  publisher = {Prentice Hall}
}
@ARTICLE{proni:jhe94,
  author = {John R. Proni and Hening Huang and William P. Dammann},
  year = 1994,
  title = {{Initial Dilution of Southeast Florida Ocean Outfalls}},
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@inproceedings{PyTorch2019,
 author = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
 booktitle = {Advances in Neural Information Processing Systems},
 editor = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
 pages = {},
 publisher = {Curran Associates, Inc.: Red Hook, NY, USA},
 title = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
 url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/bdbca288fee7f92f2bfa9f7012727740-Paper.pdf},
 volume = {32},
 year = {2019}
}
@misc{rabanser2020,
      title={The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Models}, 
      author={Stephan Rabanser and Tim Januschowski and Valentin Flunkert and David Salinas and Jan Gasthaus},
      year={2020},
      eprint={2005.10111},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
@article{RAJAPAKSHA2022,
title = {LoMEF: A framework to produce local explanations for global model time series forecasts},
journal = {International Journal of Forecasting},
year = {2022},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2022.06.006},
url = {https://www.sciencedirect.com/science/article/pii/S0169207022000978},
author = {Dilini Rajapaksha and Christoph Bergmeir and Rob J. Hyndman},
keywords = {Local interpretability, Time series forecasting, Global models, Bootstrapping, Explainability},
abstract = {Global forecasting models (GFMs) that are trained across a set of multiple time series have shown superior results in many forecasting competitions and real-world applications compared with univariate forecasting approaches. One aspect of the popularity of statistical forecasting models such as ETS and ARIMA is their relative simplicity and interpretability (in terms of relevant lags, trend, seasonality, and other attributes), while GFMs typically lack interpretability, especially relating to particular time series. This reduces the trust and confidence of stakeholders when making decisions based on the forecasts without being able to understand the predictions. To mitigate this problem, we propose a novel local model-agnostic interpretability approach to explain the forecasts from GFMs. We train simpler univariate surrogate models that are considered interpretable (e.g., ETS) on the predictions of the GFM on samples within a neighbourhood that we obtain through bootstrapping, or straightforwardly as the one-step-ahead global black-box model forecasts of the time series which needs to be explained. After, we evaluate the explanations for the forecasts of the global models in both qualitative and quantitative aspects such as accuracy, fidelity, stability, and comprehensibility, and are able to show the benefits of our approach.}
}
@INPROCEEDINGS{Ramos:5CongAgua00,
  author = {Patr{\'{\i}}cia Ramos and M{\'a}rio Valente Neves and Nuno Cruz and Fernando Lobo Pereira},
  month = {Setembro},
  year = 2000,
  title = {{Perspectivas Para a Utiliza\c c\~ao de Ve\'{\i}culos Submarinos Aut\'onomos Na Monitoriza\c c\~ao de Descargas de \'Aguas Residuais (in Portuguese)}},
  booktitle = {{CD/ROM do 5 Congresso da \'Agua}}
}
@INPROCEEDINGS{ramos:6CongAgua02,
  author = {P. Ramos and M. V. Neves and F. L. Pereira and I. Quintaneiro},
  month = {Mar\c co},
  year = 2002,
  title = {{Plano da Miss\~ao de Monitoriza\c c\~ao ao Exutor Submarino de S. Jacinto Utilizando o VSA Isurus (in Portuguese)}},
  booktitle = {{CD/ROM do 6 Congresso da \'Agua}},
  address = {Porto, Portugal}
}
@INPROCEEDINGS{ramos:7GeoEnv2008,
  author = {M. Monego and P. Ramos and M. V. Neves},
  month = {September 8-10},
  year = 2008,
  title = {Geostatistical Mapping of Outfall Plume Dispersion Data Gathered with an Autonomous Underwater Vehicle},
  booktitle = {7th International Conference on Geostatistics for Environmental Applications},
  pages = {2009-2014},
  address = { Southampton, UK}
}
@INPROCEEDINGS{ramos:bookchapter2009,
  author = {P. Ramos and M. V. Neves},
  year = 2009,
  title = {{Environmental Impact Assessment and Management of Sewage Outfall Discharges using AUVs}},
  booktitle = {Underwater Vehicles},
  pages = {417-436},
  note = {{ ISBN 978-953-7619-49-7}},
  publisher = {Prentice Hall},
  editor = {A. Inzartsev},
  address = { In-Tech, Austria}
}
@ARTICLE{ramos:csr07,
  author = {P. Ramos and M. V. Neves and F. L. Pereira},
  year = 2007,
  title = {{Mapping and Initial Dilution Estimation of a Sewage Outfall Plume using an Autonomous Underwater Vehicle}},
  journal = {{Continental Shelf Research}},
  number = 27,
  pages = {583-593}
}
@INPROCEEDINGS{ramos:iros2010,
  author = {P. Ramos and N. Abreu},
  month = {October 22},
  year = 2010,
  title = {{Environmental monitoring of wastewater discharges using an autonomous underwater vehicle}},
  booktitle = {{Workshop on Robotics for Environmental Monitoring, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS2010}},
  address = {Taipei, Taiwan}
}
@INPROCEEDINGS{ramos:isos11,
  author = {P. Ramos and N. Abreu},
  month = {May 15-18},
  year = 2011,
  title = {{Spatial analysis and dilution estimation of Foz do Arelho Outfall using observations gathered by an AUV}},
  booktitle = {{International Symposium on Outfall Systems}},
  address = {Mar del Plata, Argentina}
}
@INPROCEEDINGS{ramos:medcoast01,
  author = {P. Ramos and N. Cruz and A. Matos and M. V. Neves and F. L. Pereira},
  month = {October},
  year = 2001,
  title = {{Prediction Studies for an AUV Monitoring Mission Plan}},
  booktitle = {Fifth International Conference on the Mediterranean Coastal Environment, Medcoast 2001},
  pages = {1299-1310},
  address = {Hamammet, Tunisia}
}
@ARTICLE{ramos:mts2011,
  author = {Patr{\'{\i}}cia Ramos and Nuno Abreu},
  year = 2011,
  title = {{Using an AUV for Assessing Wastewater Discharges Impact: An Approach Based on Geostatistics}},
  journal = {Marine Technology Society Journal},
  volume = 45,
  number = 2
}
@INPROCEEDINGS{ramos:mwwd00,
  author = {P. Ramos and M. V. Neves and N. Cruz and F. L. Pereira},
  month = {{November 28 - December 1}},
  year = 2000,
  title = {{Outfall Monitoring Using Autonomous Underwater Vehicles}},
  booktitle = {{International Conference on Marine Waste Water Discharges, MWWD 2000}},
  pages = {321-331},
  address = {G\'enova, Italy}
}
@INPROCEEDINGS{ramos:mwwd02,
  author = {P. Ramos and M. V. Neves and F. L. Pereira and I. Quintaneiro},
  month = {September},
  year = 2002,
  title = {{Mapping the Aveiro Sea Outfall Plume: Sampling Strategies for
          Autonomous Underwater Vehicles}},
  booktitle = {International Conference on Marine Waste Water Discharges,
              {MWWD} 2002},
  address = {Istanbul, Turkey}
}
@INPROCEEDINGS{ramos:mwwd2010,
  author = {P. Ramos and N. Abreu},
  month = {October 25-29},
  year = 2010,
  title = {{Spatial analysis of sea outfall discharges using block kriging}},
  booktitle = {{6th International Conference on Marine Waste Water Discharges and Coastal Environment, MWWD2010}},
  address = {Langkawi, Malaysia}
}
@INPROCEEDINGS{ramos:oceans01,
  author = {P. Ramos and N. Cruz and A. Matos and M. V. Neves and F. L. Pereira},
  month = {November 5-8},
  year = 2001,
  title = {{Monitoring an Ocean Outfall Using an AUV}},
  booktitle = {{MTS/IEEE} International Conference Oceans 2001},
  pages = {2009-2014},
  address = {Honolulu, HI, USA}
}
@INPROCEEDINGS{ramos:oceans02,
  author = {P. Ramos and M. V. Neves and F. L. Pereira},
  month = {October},
  year = 2002,
  title = {{Using Near Field Model Predictions to Detect and Map an Outfall Sewage Plume with an AUV}},
  booktitle = {{MTS/IEEE} International Conference Oceans 2002},
  address = {Biloxi, Mississippi, USA}
}
@INPROCEEDINGS{ramos:oceans08,
  author = {P. Ramos and M. Monego and S. Carvalho},
  month = {September},
  year = 2008,
  title = {{Spatial Distribution of a Sewage Outfall Plume Observed with an AUV}},
  booktitle = {{MTS/IEEE} International Conference Oceans 2008},
  address = {Qu\'ebec, Canada}
}
@INPROCEEDINGS{ramos:oceans10a,
  author = {N. Abreu and A. Matos and P. Ramos and N. Cruz},
  month = {September 20-23},
  title = {{Automatic interface for AUV mission planning and supervision}},
  booktitle = {{MTS/IEEE} International Conference Oceans 2010},
  address = {Seattle, USA}
}
@INPROCEEDINGS{ramos:oceans10b,
  author = {N. Abreu and P. Ramos},
  month = {September 20-23},
  year = 2010,
  title = {{An integrated application for geostatistical analysis of sea outfall discharges based on R software}},
  booktitle = {{MTS/IEEE} International Conference Oceans 2010},
  address = {Seattle, USA}
}
@INPROCEEDINGS{ramos:oceans11,
  author = {P. Ramos and N. Abreu},
  month = {June 6-9},
  year = 2011,
  title = {{Environmental impact assessment of Foz do Arelho sewage plume using MARES AUV}},
  booktitle = {{IEEE/OES International Conference Oceans 2011}},
  address = {Santander, Spain}
}
@ARTICLE{ramos:revistarobotica01,
  author = {Patr{\'{\i}}cia Ramos et Al.},
  year = 2001,
  title = {{Miss\~oes de Monitoriza\c c\~ao Ambiental Utilizando O
          Ve\'{\i}culo Submarino Aut\'onomo Isurus}},
  journal = {{Revista Rob\'otica - Automa\c c\~ao, Controlo e
            Instrumenta\c c\~ao}},
  number = 44,
  pages = {78-82}
}
@article{Ramos15,
title = {{Performance of state space and ARIMA models for consumer retail sales forecasting}},
journal = "Robotics and Computer-Integrated Manufacturing",
volume = "34",
pages = "151-163",
year = "2015",
issn = "0736-5845",
doi = "10.1016/j.rcim.2014.12.015",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0736584515000137",
author = "Patrícia Ramos and Nicolau Santos and Rui Rebelo",
keywords = "Aggregate retail sales, Forecast accuracy, State space models, ARIMA models",
abstract = "Forecasting future sales is one of the most important issues that is beyond all strategic and planning decisions in effective operations of retail businesses. For profitable retail businesses, accurate demand forecasting is crucial in organizing and planning production, purchasing, transportation and labor force. Retail sales series belong to a special type of time series that typically contain trend and seasonal patterns, presenting challenges in developing effective forecasting models. This work compares the forecasting performance of state space models and ARIMA models. The forecasting performance is demonstrated through a case study of retail sales of five different categories of women footwear: Boots, Booties, Flats, Sandals and Shoes. On both methodologies the model with the minimum value of Akaike's Information Criteria for the in-sample period was selected from all admissible models for further evaluation in the out-of-sample. Both one-step and multiple-step forecasts were produced. The results show that when an automatic algorithm the overall out-of-sample forecasting performance of state space and ARIMA models evaluated via RMSE, MAE and MAPE is quite similar on both one-step and multi-step forecasts. We also conclude that state space and ARIMA produce coverage probabilities that are close to the nominal rates for both one-step and multi-step forecasts."
}
@article{Ramos16,
title = {{A procedure for identification of appropriate state space and ARIMA models based on time-series cross-validation}},
journal = "Algorithms",
volume = "9",
number = "4",
year = "2016",
pages="76",
author = "Patrícia Ramos and José Manuel Oliveira",
doi = {10.3390/a9040076},
IGNOREurl = {https://www.mdpi.com/1999-4893/9/4/76}
}
@inproceedings{Ramos2016AMT,
Author={Ramos, P and Oliveira, JM and Rebelo, R},
Title={{Evaluating the Forecasting Accuracy of Pure Time Series Models on Retail Data}},
Year={{2016}},
Series={{Advances in Transdisciplinary Engineering}},
Editor={Goh, Ym and Case, K},
Booktitle={{Advances in Manufacturing Technology XXX}},
Volume={{3}},
Pages={{489-494}},
DOI={10.3233/978-1-61499-668-2-489}, 
Type={Proceedings Paper}
}
@Article{Ramos2022,
AUTHOR = {Ramos, Patrícia and Oliveira, José Manuel and Kourentzes, Nikolaos and Fildes, Robert},
TITLE = {Forecasting Seasonal Sales with Many Drivers: Shrinkage or Dimensionality Reduction?},
JOURNAL = {Applied System Innovation},
VOLUME = {6},
YEAR = {2023},
NUMBER = {1},
ARTICLE-NUMBER = {3},
IGNOREurl = {https://www.mdpi.com/2571-5577/6/1/3},
ISSN = {2571-5577},
ABSTRACT = {Retailers depend on accurate forecasts of product sales at the Store &times; SKU level to efficiently manage their inventory. Consequently, there has been increasing interest in identifying more advanced statistical techniques that lead to accuracy improvements. However, the inclusion of multiple drivers affecting demand into commonly used ARIMA and ETS models is not straightforward, particularly when many explanatory variables are available. Moreover, regularization regression models that shrink the model&rsquo;s parameters allow for the inclusion of a lot of relevant information but do not intrinsically handle the dynamics of the demand. These problems have not been addressed by previous studies. Nevertheless, multiple simultaneous effects interacting are common in retailing. To be successful, any approach needs to be automatic, robust and efficiently scaleable. In this study, we design novel approaches to forecast retailer product sales taking into account the main drivers which affect SKU demand at store level. To address the variable selection challenge, the use of dimensionality reduction via principal components analysis (PCA) and shrinkage estimators was investigated. The empirical results, using a case study of supermarket sales in Portugal, show that both PCA and shrinkage are useful and result in gains in forecast accuracy in the order of 10\% over benchmarks while offering insights on the impact of promotions. Focusing on the promotional periods, PCA-based models perform strongly, while shrinkage estimators over-shrink. For the non-promotional periods, shrinkage estimators significantly outperform the alternatives.},
DOI = {10.3390/asi6010003}
}
@ARTICLE{rawn:ASCETrans61,
  author = {A. M. Rawn and F. R. Bowerman and M. Hon and N. H. Brooks},
  year = 1961,
  title = {{Diffusers for Disposal of Sewage in Sea Water}},
  journal = {Transactions of the American Society of Civil Engineers},
  volume = 126,
  pages = {344-388},
  note = {{Part III}}
}
@ARTICLE{rawn:transactions30,
  author = {Rawn, A. M. and Palmer, H. K.},
  year = 1930,
  title = {{Predetermining the Extent of a Sewage Field in Seawater}},
  journal = {Transactions, ASCE},
  volume = 94,
  pages = {1036-1060}
}
@INPROCEEDINGS{remus:procAUV94,
  author = {C. Von Alt and B. Allen and T. Austin and R. Stokey},
  year = 1994,
  title = {{Remote Environmental Measuring Units}},
  booktitle = {Proceedings of the Autonomous Underwater Vehicle's 94 Conference},
  pages = {13-19},
  organization = {Cambridge, MA, USA}
}
@INPROCEEDINGS{ribeiro:14SINAPE,
  author = {P.J. Diggle and P. J. Ribeiro},
  year = 2000,
  title = {Model-based geostatistics},
  booktitle = {14th SINAPE - Simp\'osio Nacional de Probabilidade e Estat\'istica.}
}
@inproceedings{Ribeiro2016AMT,
Author={Ribeiro, C and Oliveira, JM and Ramos, P},
Title={{Management of Promotional Activity Supported by Forecasts Based on Assorted Information}},
Year={{2016}},
Series={{Advances in Transdisciplinary Engineering}},
Editor={Goh, Ym and Case, K},
Booktitle={{Advances in Manufacturing Technology XXX}},
Volume={{3}},
Pages={{477-482}},
doi={10.3233/978-1-61499-668-2-477}, 
Type={Proceedings Paper}
}       
@ARTICLE{richardson:26,
  author = {Richardson, L. F.},
  year = 1926,
  title = {{Atmospheric Diffusion Shown on a Distance-Neighbor Graph}},
  journal = {Proc. Roy. Soc. London},
  volume = {A110},
  pages = {709-739}
}
@article{Riley2021,
title = {Penalization and shrinkage methods produced unreliable clinical prediction models especially when sample size was small},
journal = {Journal of Clinical Epidemiology},
volume = {132},
pages = {88-96},
year = {2021},
issn = {0895-4356},
doi = {10.1016/j.jclinepi.2020.12.005},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0895435620312099},
author = {Richard D. Riley and Kym I.E. Snell and Glen P. Martin and Rebecca Whittle and Lucinda Archer and Matthew Sperrin and Gary S. Collins},
keywords = {Risk prediction models, Penalization, Shrinkage, Overfitting, Sample size},
abstract = {Objectives
When developing a clinical prediction model, penalization techniques are recommended to address overfitting, as they shrink predictor effect estimates toward the null and reduce mean-square prediction error in new individuals. However, shrinkage and penalty terms (‘tuning parameters’) are estimated with uncertainty from the development data set. We examined the magnitude of this uncertainty and the subsequent impact on prediction model performance.
Study Design and Setting
This study comprises applied examples and a simulation study of the following methods: uniform shrinkage (estimated via a closed-form solution or bootstrapping), ridge regression, the lasso, and elastic net.
Results
In a particular model development data set, penalization methods can be unreliable because tuning parameters are estimated with large uncertainty. This is of most concern when development data sets have a small effective sample size and the model's Cox-Snell R2 is low. The problem can lead to considerable miscalibration of model predictions in new individuals.
Conclusion
Penalization methods are not a ‘carte blanche’; they do not guarantee a reliable prediction model is developed. They are more unreliable when needed most (i.e., when overfitting may be large). We recommend they are best applied with large effective sample sizes, as identified from recent sample size calculations that aim to minimize the potential for model overfitting and precisely estimate key parameters.}
}
@INBOOK{roberts:bookASCE02,
  author = {Philip J. W. Roberts and Donald R. Webster},
  year = 2002,
  title = {{Environmental Fluid Mechanics - Theories and Applications}},
  edition = {{Hayley H. Shen and Alexander H. D. Chen and Keh-Han Wang and Michelle H. Teng and Clark C. K. Liu}},
  chapter = {Turbulent Diffusion},
  pages = 467,
  note = {ISBN: 0-7844-0629-4},
  publisher = {ASCE}
}
@INPROCEEDINGS{roberts:conf90,
  author = {Philip J. W. Roberts and David Wilson},
  month = {July 30 - August 3},
  year = 1990,
  title = {{Field and Model Studies of Ocean Outfalls}},
  booktitle = {Hydraulic Engineering Proceedings, 1990 National Conference},
  publisher = {ASCE},
  address = {H. Chang, New York, San Diego}
}
@ARTICLE{roberts:cr91,
  author = {Philip J. W. Roberts},
  year = 1991,
  title = {{Ocean Outfalls}},
  journal = {Critical Reviews in Environmental Control},
  volume = 20,
  number = {5{\&}6},
  pages = {311-339},
  note = {{Conrad P. Straub, Ed., CRC Press}}
}
@ARTICLE{roberts:jee97,
  author = {Philip J. W. Roberts and Rafael Sternau},
  year = 1997,
  title = {{Mixing Zone Analysis for Coastal Wastewater Discharge}},
  journal = {Journal of Environmental Engineering},
  volume = 123,
  number = 12,
  pages = {1244-1250}
}
@ARTICLE{roberts:jhd79a,
  author = {Philip J. W. Roberts},
  year = 1979,
  title = {{Line Plume and Ocean Outfall Dispersion}},
  journal = {Journal of the Hydraulics Division, ASCE},
  volume = 105,
  number = {HY4},
  pages = {313-331}
}
@ARTICLE{roberts:jhd79b,
  author = {Philip J. W. Roberts},
  year = 1979,
  title = {{Two-Dimensional Flow Field of Multiport Diffuser}},
  journal = {Journal of the Hydraulics Division, ASCE},
  volume = 105,
  number = {HY5},
  pages = {607-611}
}
@ARTICLE{roberts:jhd80,
  author = {Philip J. W. Roberts},
  year = 1980,
  title = {{Ocean Outfall Dilution: Effects of Currents}},
  journal = {Journal of the Hydraulics Division, ASCE},
  volume = 106,
  number = {HY5},
  pages = {769-782}
}
@ARTICLE{roberts:jhe87,
  author = {Philip J. W. Roberts and G. Toms},
  year = 1987,
  title = {{Inclined Dense Jets in Flowing Current}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 113,
  number = 3,
  pages = {323-341}
}
@ARTICLE{roberts:jhe88,
  author = {Philip J. W. Roberts},
  year = 1988,
  title = {{Ocean Outfall System for Dense and Buoyant Effluents}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 114,
  number = 5,
  pages = {1175-1191}
}
@ARTICLE{roberts:jhe89a,
  author = {Philip J. W. Roberts and W. H. Snyder and D. J. Baumgartner},
  editor = {ASCE},
  month = jan,
  year = 1989,
  title = {{Ocean Outfalls. I: Submerged Wastefield Formation}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 115,
  number = 1,
  pages = {1-25}
}
@ARTICLE{roberts:jhe89b,
  author = {Philip J. W. Roberts and W. H. Snyder and D. J. Baumgartner},
  editor = {ASCE},
  month = jan,
  year = 1989,
  title = {{Ocean Outfalls. II: Spatial Evolution of Submerged Wastefield}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 115,
  number = 1,
  pages = {26-48}
}
@ARTICLE{roberts:jhe89c,
  author = {Philip J. W. Roberts and W. H. Snyder and D. J. Baumgartner},
  editor = {ASCE},
  month = jan,
  year = 1989,
  title = {{Ocean Outfalls. III: Effect of Diffuser Design on Submerged
          Wastefield}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 115,
  number = 1,
  pages = {49-70}
}
@ARTICLE{roberts:jhe93a,
  author = {Philip J. W. Roberts},
  year = 1993,
  title = {{Hydraulic Model Study for Boston Outfall. I: Riser Configuration}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 119,
  number = 9,
  pages = {970-987}
}
@ARTICLE{roberts:jhe93b,
  author = {Philip J. W. Roberts},
  year = 1993,
  title = {{Hydraulic Model Study for Boston Outfall. II: Environmental
          Performance}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 119,
  number = 9,
  pages = {988-1002}
}
@ARTICLE{roberts:jhe97,
  author = {Philip J. W. Roberts and Adrian Ferrier and Greg Daviero},
  year = 1997,
  title = {{Mixing in Inclined Dense Jets}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 123,
  number = 8,
  pages = {693-699}
}
@ARTICLE{roberts:jhe99a,
  author = {Philip J. W. Roberts},
  editor = {ASCE},
  month = {June},
  year = 1999,
  title = {{Modeling Mamala Bay Outfall Plumes. I: Near Field}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 125,
  number = 6,
  pages = {564-573}
}
@ARTICLE{roberts:jhe99b,
  author = {Philip J. W. Roberts},
  editor = {ASCE},
  month = {June},
  year = 1999,
  title = {{Modeling Mamala Bay Outfall Plumes. II: Far Field}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 125,
  number = 6,
  pages = {574-583}
}
@ARTICLE{roberts:jhe01,
  author = {Philip J. W. Roberts and Karen Maile and Greg Daviero},
  year = 2001,
  title = {{Mixing in Stratified Jets}},
  journal = {Journal of Hydraulic Engineering, ASCE},
  volume = 127,
  number = 3,
  pages = {194-200}
}
@ARTICLE{roberts:mer2010,
  author = {Carlton D. Hunt and Alex D. Mansfield and Michael J. Mickelson and Carl S. Albro and W. Rockwell Geyer and Philip J. W. Roberts},
  year = 2010,
  title = {{Plume tracking and dilution of effluent from the Boston sewage outfall}},
  journal = {Marine Environmental Research},
  volume = 70,
  pages = {150-161}
}
@INPROCEEDINGS{roberts:mwwd02,
  author = {P. J. W. Roberts and Carlton D. Hunt and Michael J. Mickelson},
  month = {September 16-21},
  year = 2002,
  title = {{Field and Model Studies of the Boston Outfall}},
  booktitle = {Proceedings of the 2nd International Conference on Marine Waste Water Discharges},
  note = {{MWWD} 2002}
}
@INBOOK{roberts:nato86,
  author = {Roberts, P. J. W.},
  year = 1986,
  title = {{The Role of the Oceans as a Waste Disposal Option}},
  edition = {{G. Kullenberg}},
  volume = 172,
  chapter = {Engineering of Ocean Outfalls},
  pages = {73-109},
  publisher = {{NATO ASI Series C}}
}
@INCOLLECTION{roberts:nato94,
  author = {P. J. W. Roberts},
  editor = {P. A. Davies and M. J. Valente Neves},
  year = 1994,
  title = {{Jets and Plumes and Ocean Outfall Design}},
  booktitle = {Recent Research in the Fluid Mechanics of Turbulent Jets and Plumes},
  pages = {441-464},
  publisher = {Kluwer Academic Publishers},
  address = {Dordrecht, Boston, London}
}
@PHDTHESIS{roberts:phd77,
  author = {Philip J. W. Roberts},
  year = 1977,
  title = {{Dispersion of Buoyant Waste Water Discharged from Outfall Diffusers of Finite Length}},
  school = {California Institute of Technology, Pasadena}
}
@INPROCEEDINGS{roberts:proc79,
  author = {Philip J. W. Roberts},
  year = 1979,
  title = {{A Mathematical Model of Initial Dilution for Deepwater Ocean Outfalls}},
  booktitle = {Proceedings of the Specialty Conference on Conservation and Utilization of Water and Energy Resources},
  pages = {218-225},
  address = {San Francisco}
}
@INBOOK{roberts:seaoutfalls96,
  author = {Philip J. W. Roberts},
  year = 1996,
  title = {{Environmental Hydraulics}},
  edition = {{V. P. Singh and W. H. Hager}},
  chapter = {Sea Outfalls},
  pages = {63-110},
  publisher = {Kluwer Academic Press Publishers},
  address = {Printed in Netherlands}
}
@ARTICLE{roberts:wst86,
  author = {Philip J. W. Roberts},
  year = 1986,
  title = {{The Use of Current Data in Ocean Outfall Design}},
  journal = {Water Science and Technology},
  volume = 18,
  pages = {111-120}
}
@ARTICLE{roberts:wst89,
  author = {Philip J. W. Roberts},
  year = 1989,
  title = {{Dilution and Transport Predictions for Ocean Outfalls}},
  journal = {Water Science and Technology},
  volume = 21,
  pages = {969-979}
}
@ARTICLE{roberts:wst92,
  author = {Philip J. W. Roberts and N. Williams},
  year = 1992,
  title = {{Modeling of Ocean Outfall Discharges}},
  journal = {Water Science and Technology},
  volume = 25,
  number = 9,
  pages = {155-164}
}
@ARTICLE{roberts:wst95,
  author = {Philip J. W. Roberts},
  editor = {Pergamon Press},
  year = 1995,
  title = {{Near-Field Modeling of the Mamala Bay Outfalls}},
  journal = {Water Science and Technology},
  volume = 32,
  number = 2,
  pages = {159-166},
  note = {0273-1223(95)00581-1}
}
@ARTICLE{roberts:wst98,
  author = {Philip J. W. Roberts},
  year = 1998,
  title = {{Far Field Modeling of the Mamala Bay Outfalls}},
  journal = {Water Science and Technology},
  volume = 38,
  number = 10,
  pages = {323-330}
}
@INPROCEEDINGS{Robinson:LOOPS99,
  author = {A.R. Robinson and J.G. Bellingham and C. Chryssostomidis and T.D. Dickey and E. Levine and N. Petrikalakis and D.L. Porter and B.J. Rothschild and H. Schmidt and K. Sherman and D.V. Holliday and D.K. Atwood},
  year = 1999,
  title = {Real-Time Forecasting of the Multidisciplinary Coastal Ocean with the Littoral Ocean Observing and Predicting System {(LOOPS)}},
  booktitle = {Proceedings of the Third Conference on Coastal Atmospheric and Oceanic Prediction Processes},
  publisher = {American Meteorological Society},
  address = {New Orleans, LA}
}
@ARTICLE{Rogowski,
  author = {P. Rogowski and E. J. Terrill and M. P. Otero and L. Hazard and W. F. Middleton},
  year = 2012,
  title = {Mapping Ocean Outfall Plumes and their Mixing using Autonomous Underwater Vehicles},
  journal = {J. Geophys. Res.},
  note = {in press}
}
@INPROCEEDINGS{roldao:iwa00,
  author = {Jo{\~a}o Rold{\~a}o and Jo{\~a}o L. B. Carvalho and Philip J. W. Roberts},
  year = 2000,
  title = {{Field Observations of Dilution on the Ipanema Beach Outfall}},
  booktitle = {1st World Congress of the International Water},
  publisher = {International Water Association},
  address = {Paris}
}
@ARTICLE{roldao:watersciencetech01,
  author = {J. Rold{\~a}o and J. L. B. Carvalho and P. J. W. Roberts},
  editor = {IWA},
  year = 2001,
  title = {{Field Observations of Dilution on the Ipanema Beach Outfall}},
  journal = {Water Science and Tecnology},
  volume = 43,
  number = 11,
  pages = {351-360}
}
@ARTICLE{rouse:tellus52,
  author = {Rouse, H. and Yih, C. S. and Humphreys, H. W.},
  year = 1952,
  title = {{Gravitational Convection from a Boundary Source}},
  journal = {Tellus},
  volume = 4,
  pages = {201-210}
}
@Manual{RSoftware,
title = {R: A Language and Environment for Statistical Computing},
author = {{R Development Core Team}},
organization = {R Foundation for Statistical Computing},
address = {Vienna, Austria},
year = {2019},
url = {http://www.R-project.org},
}
@article{Sagaert2018,
title = {Tactical sales forecasting using a very large set of macroeconomic indicators},
journal = {European Journal of Operational Research},
volume = {264},
number = {2},
pages = {558-569},
year = {2018},
issn = {0377-2217},
doi = {10.1016/j.ejor.2017.06.054},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0377221717305957},
author = {Yves R. Sagaert and El-Houssaine Aghezzaf and Nikolaos Kourentzes and Bram Desmet},
keywords = {Forecasting, Tactical planning, Leading indicators, LASSO, Variable selection},
abstract = {Tactical forecasting in supply chain management supports planning for inventory, scheduling production, and raw material purchase, amongst other functions. It typically refers to forecasts up to 12 months ahead. Traditional forecasting models take into account univariate information extrapolating from the past, but cannot anticipate macroeconomic events, such as steep increases or declines in national economic activity. In practice this is countered by using managerial expert judgement, which is well known to suffer from various biases, is expensive and not scalable. This paper evaluates multiple approaches to improve tactical sales forecasting using macro-economic leading indicators. The proposed statistical forecast selects automatically both the type of leading indicators, as well as the order of the lead for each of the selected indicators. However as the future values of the leading indicators are unknown an additional uncertainty is introduced. This uncertainty is controlled in our methodology by restricting inputs to an unconditional forecasting setup. We compare this with the conditional setup, where future indicator values are assumed to be known and assess the theoretical loss of forecast accuracy. We also evaluate purely statistical model building against judgement aided models, where potential leading indicators are pre-filtered by experts, quantifying the accuracy-cost trade-off. The proposed framework improves on forecasting accuracy over established time series benchmarks, while providing useful insights about the key leading indicators. We evaluate the proposed approach on a real case study and find 18.8\% accuracy gains over the current forecasting process.}
}
@article{Salinas2020,
title = {{DeepAR: Probabilistic forecasting with autoregressive recurrent networks}},
journal = {International Journal of Forecasting},
volume = {36},
number = {3},
pages = {1181-1191},
year = {2020},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2019.07.001},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207019301888},
author = {David Salinas and Valentin Flunkert and Jan Gasthaus and Tim Januschowski}
}
@article{Schaeffer2020,
title = {{Forecasting client retention — A machine-learning approach}},
journal = {Journal of Retailing and Consumer Services},
volume = {52},
pages = {101918},
year = {2020},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2019.101918},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698919302668},
author = {Satu Elisa Schaeffer and Sara Veronica {Rodriguez Sanchez}},
keywords = {Client retention, Sales forecasting, Machine learning, Prepaid unitary services},
abstract = {In the age of big data, companies store practically all data on any client transaction. Making use of this data is commonly done with machine-learning techniques so as to turn it into information that can be used to drive business decisions. Our interest lies in using data on prepaid unitary services in a business-to-business setting to forecast client retention: whether a particular client is at risk of being lost before they cease being clients. The purpose of such a forecast is to provide the company with an opportunity to reach out to such clients as an effort to ensure their retention. We work with monthly records of client transactions: each client is represented as a series of purchases and consumptions. We vary (1) the length of the time period used to make the forecast, (2) the length of a period of inactivity after which a client is assumed to be lost, and (3) how far in advance the forecast is made. Our experimental work finds that current machine-learning techniques able to adequately predict, well in advance, which clients will be lost. This knowledge permits a company to focus marketing efforts on such clients as early as three months in advance.}
}
@article{Schaer2019,
title = {Demand forecasting with user-generated online information},
journal = {International Journal of Forecasting},
volume = {35},
number = {1},
pages = {197-212},
year = {2019},
IGNOREnote = {Special Section: Supply Chain Forecasting},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2018.03.005},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207018300505},
author = {Oliver Schaer and Nikolaos Kourentzes and Robert Fildes},
keywords = {Google trends, Social media, Leading indicators, Product life-cycle, Search traffic, Electronic word-of-mouth},
abstract = {Recently, there has been substantial research on the augmentation of aggregate forecasts with individual consumer data from internet platforms, such as search traffic or social network shares. Although the majority of studies have reported increases in accuracy, many exhibit design weaknesses, including a lack of adequate benchmarks or rigorous evaluation. Furthermore, their usefulness over the product life-cycle has not been investigated, even though this may change, as consumers may search initially for pre-purchase information, but later for after-sales support. This study begins by reviewing the relevant literature, then attempts to support the key findings using two forecasting case studies. Our findings are in stark contrast to those in the previous literature, as we find that established univariate forecasting benchmarks, such as exponential smoothing, consistently perform better those that include online information. Our research underlines the need for a thorough forecast evaluation and argues that the usefulness of online platform data for supporting operational decisions may be limited.}
}
@article{Schafer05,
title = "A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics",
journal = "Statistical Applications in Genetics and Molecular Biology",
volume = "4",
number = "1",
pages = "151 - 163",
year = "2005",
author = "Juliane Sch{\"a}fer and Korbinian Strimmer",
note = {doi:10.2202/1544-6115.1175},
doi = {10.2202/1544-6115.1175}
}
@article{Schwarz78,
author = "Gideon Schwarz",
title = "Estimating the Dimension of a Model",
journal = "The Annals of Statistics",
volume = "6",
number = "2",
pages = "461-464",
year = "1978"
}
@article{Seaman2018,
title = {Considerations of a retail forecasting practitioner},
journal = {International Journal of Forecasting},
volume = {34},
number = {4},
pages = {822-829},
year = {2018},
issn = {0169-2070},
doi = {10.1016/j.ijforecast.2018.03.001},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207018300293},
author = {Brian Seaman}
}
@article{SEAMAN20221468,
title = {Applicability of the {M5} to Forecasting at Walmart},
journal = {International Journal of Forecasting},
volume = {38},
number = {4},
pages = {1468-1472},
year = {2022},
note = {Special Issue: M5 competition},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2021.06.002},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207021001023},
author = {Brian Seaman and John Bowman},
keywords = {Retail Forecasting, M5 Competition, Practitioner, Walmart, Metrics},
abstract = {The M5 Forecasting Competition, the fifth in the series of forecasting competitions organized by Professor Spyros Makridakis and the Makridakis Open Forecasting Center at the University of Nicosia, was an extremely successful event. This competition focused on both the accuracy and uncertainty of forecasts and leveraged actual historical sales data provided by Walmart. This has led to the M5 being a unique competition that closely parallels the difficulties and challenges associated with industrial applications of forecasting. Like its precursor the M4, many interesting ideas came from the results of the M5 competition which will continue to push forecasting in new directions. In this article we discuss four topics around the practitioners view of the application of the competition and its results to the actual problems we face. First, we examine the data provided and how it relates to common difficulties practitioners must overcome. Secondly, we review the relevance of the accuracy and uncertainty metrics associated with the competition. Third, we discuss the leading solutions and their implications to forecasting at a company like Walmart. We then close with thoughts about a future M6 competition and further enhancements that can be explored.}
}
@article{SEMENOGLOU20211072,
title = {Investigating the accuracy of cross-learning time series forecasting methods},
journal = {International Journal of Forecasting},
volume = {37},
number = {3},
pages = {1072-1084},
year = {2021},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2020.11.009},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207020301850},
author = {Artemios-Anargyros Semenoglou and Evangelos Spiliotis and Spyros Makridakis and Vassilios Assimakopoulos},
keywords = {Time series, Cross-learning, Features, Neural networks, M4 competition},
abstract = {The M4 competition identified innovative forecasting methods, advancing the theory and practice of forecasting. One of the most promising innovations of M4 was the utilization of cross-learning approaches that allow models to learn from multiple series how to accurately predict individual ones. In this paper, we investigate the potential of cross-learning by developing various neural network models that adopt such an approach, and we compare their accuracy to that of traditional models that are trained in a series-by-series fashion. Our empirical evaluation, which is based on the M4 monthly data, confirms that cross-learning is a promising alternative to traditional forecasting, at least when appropriate strategies for extracting information from large, diverse time series data sets are considered. Ways of combining traditional with cross-learning methods are also examined in order to initiate further research in the field.}
}
@article{Shang2017,
author = {Han Lin Shang and Rob J. Hyndman},
title = {Grouped Functional Time Series Forecasting: An Application to Age-Specific Mortality Rates},
journal = {Journal of Computational and Graphical Statistics},
volume = {26},
number = {2},
pages = {330-343},
year  = {2017},
publisher = {Taylor & Francis},
IGNOREurl = {https://www.tandfonline.com/doi/full/10.1080/10618600.2016.1237877},
doi = {10.1080/10618600.2016.1237877}
}
@article{Shlifer79,
author = {E. Shlifer and R. W. Wolff},
journal = {Management Science},
number = {6},
publisher = {Informs},
title = {Aggregation and Proration in Forecasting},
volume = {25},
year = {1979},
pages = {594-603},
note = {doi:10.1287/mnsc.25.6.594},
doi = {10.1287/mnsc.25.6.594}
}
@BOOK{Shumway11,
  author = {R. H. Shumway and D. S. Stoffer},
  publisher = {New York: Springer, 3rd edition},
  year = 2011,
  title = {{Time series analysis and its applications: with R examples}}
}
@ARTICLE{signell:ecss00,
  author = {R. P. Signell and H. L. Jenter and A. F. Blumberg},
  year = 2000,
  title = {{Predicting the Physical Effects of Relocating Boston's Sewage Outfall}},
  journal = {Estuarine, Coastal and Shelf Science},
  volume = 50,
  pages = {59-72}
}
@MANUAL{Simria:IQ02,
  author = {Isabel Quintaneiro and Lu{\'{\i}}s Catarino},
  year = 2002,
  title = {{Uma Estrat\'egia de Evolu\c c\~ao no Dom\'{\i}nio das \'Aguas Residuais}}
}
@article{SMYL2020,
title = {A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting},
journal = {International Journal of Forecasting},
volume = {36},
number = {1},
pages = {75-85},
year = {2020},
note = {M4 Competition},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2019.03.017},
url = {https://www.sciencedirect.com/science/article/pii/S0169207019301153},
author = {Slawek Smyl},
keywords = {Forecasting competitions, M4, Dynamic computational graphs, Automatic differentiation, Long short term memory (LSTM) networks, Exponential smoothing},
abstract = {This paper presents the winning submission of the M4 forecasting competition. The submission utilizes a dynamic computational graph neural network system that enables a standard exponential smoothing model to be mixed with advanced long short term memory networks into a common framework. The result is a hybrid and hierarchical forecasting method.}
}
@article{Sodhi2011,
title = "The incremental bullwhip effect of operational deviations in an arborescent supply chain with requirements planning",
journal = "European Journal of Operational Research",
volume = "215",
number = "2",
pages = "374 - 382",
year = "2011",
issn = "0377-2217",
doi = "10.1016/j.ejor.2011.06.019",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0377221711005480",
author = "ManMohan S. Sodhi and Christopher S. Tang"
}
@article{Srinivasan04,
author = {Srinivasan, Shuba and Pauwels, Koen and Hanssens, Dominique and Dekimpe, Marnik},
year = {2004},
month = {05},
pages = {617-629},
title = {Do Promotions Benefit Manufacturers, Retailers, or Both?},
volume = {50},
journal = {Management Science},
doi = {10.1287/mnsc.1040.0225}
}
@article{spavound2022making,
  title={Making Forecasts More Trustworthy},
  author={Spavound, Simon and Kourentzes, Nikolaos and others},
  journal={Foresight: The International Journal of Applied Forecasting},
  number={66},
  pages={21--25},
  year={2022},
  publisher={International Institute of Forecasters}
}
@article{Spiliotis2021,
title = {Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data},
journal = {International Journal of Production Economics},
volume = {240},
pages = {108237},
year = {2021},
issn = {0925-5273},
doi = {10.1016/j.ijpe.2021.108237},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0925527321002139},
author = {Evangelos Spiliotis and Spyros Makridakis and Anastasios Kaltsounis and Vassilios Assimakopoulos}
}
@article{sroginis2019use,
  title={Use of contextual and model-based information in behavioural operations},
  author={Sroginis, Anna and Fildes, Robert and Kourentzes, Nikolaos},
  journal={Available at SSRN 3466929},
  year={2019},
  doi = {http://doi.org/10.2139/ssrn.3466929}
}
@misc{statsforecast2022,
    author={Federico Garza and Max Mergenthaler Canseco and Cristian Challú and Kin G. Olivares},
    title = {{StatsForecast}: Lightning fast forecasting with statistical and econometric models},
    year={2022},
    howpublished={{PyCon} Salt Lake City, Utah, US 2022},
    url={https://github.com/Nixtla/statsforecast}
}
@BOOK{Stein,
  author = {M. L. Stein},
  editor = {Springer},
  year = 1999,
  title = {{Interpolation of spatial data: some theory for kriging}},
  address = {New York}
}
@article{Stone77,
  author = {M. Stone},
  title = {An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike's Criterion},
  journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
  volume = {39},
  number = {1},  
  pages = {44-47},
  year = {1977}
}
@article{SUN2022,
title = {Multi-step ahead tourism demand forecasting: The perspective of the learning using privileged information paradigm},
journal = {Expert Systems with Applications},
volume = {210},
pages = {118502},
year = {2022},
issn = {0957-4174},
doi = {10.1016/j.eswa.2022.118502},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0957417422015858},
author = {Shaolong Sun and Mingchen Li and Shouyang Wang and Chengyuan Zhang},
keywords = {Tourism demand, Multi-step ahead forecasting, Privileged information, Machine learning, Kernel random vector functional link network},
abstract = {Accurate tourism demand forecasting can provide effective guidance for government management and tourism planning. Specifically, multi-step ahead tourism demand forecasting is of great relevance in guiding managers to develop strategies and assisting operators in business planning and is therefore of great interest to researchers and practitioners. However, it is usually difficult to obtain satisfactory accuracy in views of sophisticated data characteristics. This study proposes an improved machine learning paradigm, introducing valuable additional information into the training phase, which is not available for forecasting in the testing phase. Taking Hawaii (Daily and Weekly) and Macau tourist arrivals as the samples, the empirical evidence indicates that the proposed approach can significantly enhance the multi-step ahead forecasting performance from the view of both error calculation and statistical test. In particular, the paradigm’s robustness is also demonstrated in the comparison of sliding window predictions with models of variants.}
}
@Manual{Svetunkov2018,
  author = {Ivan Svetunkov},
  year = 2018,
  title = {{smooth: Forecasting using smoothing functions. R package version 2.4.0}},
  url = {https://CRAN.R-project.org/package=smooth}
}
@article{Syntetos2005,
title = "The accuracy of intermittent demand estimates",
journal = "International Journal of Forecasting",
volume = "21",
number = "2",
pages = "303 - 314",
year = "2005",
issn = "0169-2070",
doi = "10.1016/j.ijforecast.2004.10.001",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0169207004000792",
author = "Aris A. Syntetos and John E. Boylan",
keywords = "Demand forecasting, Intermittent demand, Accuracy measures, Croston's method, Exponential smoothing, Forecasting competition",
abstract = "Intermittent demand appears sporadically, with some time periods showing no demand at all. In this paper, four forecasting methods, Simple Moving Average (SMA, 13 periods), Single Exponential Smoothing (SES), Croston's method, and a new method (based on Croston's approach) recently developed by the authors, are compared on 3000 real intermittent demand data series from the automotive industry. The mean signed and relative geometric root-mean-square errors are shown to meet the theoretical and practical requirements of intermittent demand, as do the Percentage Better and Percentage Best summary statistics based on these measures. These measures are subsequently applied in a simulation experiment. The out-of-sample comparison results indicate superior performance of the new method. In addition, the results show that the mean signed error is not strongly scale dependent and the relative geometric root-mean-square error is a well-behaved accuracy measure for intermittent demand."
}
@article{Syntetos2016,
title = "Supply chain forecasting: Theory, practice, their gap and the future",
journal = "European Journal of Operational Research",
volume = "252",
number = "1",
pages = "1 - 26",
year = "2016",
issn = "0377-2217",
doi = "10.1016/j.ejor.2015.11.010",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0377221715010231",
author = "Aris A. Syntetos and Zied Babai and John E. Boylan and Stephan Kolassa and Konstantinos Nikolopoulos",
keywords = "Supply chain forecasting, Forecasting software, Forecasting empirical research, Literature review",
abstract = "Supply Chain Forecasting (SCF) goes beyond the operational task of extrapolating demand requirements at one echelon. It involves complex issues such as supply chain coordination and sharing of information between multiple stakeholders. Academic research in SCF has tended to neglect some issues that are important in practice. In areas of practical relevance, sound theoretical developments have rarely been translated into operational solutions or integrated in state-of-the-art decision support systems. Furthermore, many experience-driven heuristics are increasingly used in everyday business practices. These heuristics are not supported by substantive scientific evidence; however, they are sometimes very hard to outperform. This can be attributed to the robustness of these simple and practical solutions such as aggregation approaches for example (across time, customers and products). This paper provides a comprehensive review of the literature and aims at bridging the gap between theory and practice in the existing knowledge base in SCF. We highlight the most promising approaches and suggest their integration in forecasting support systems. We discuss the current challenges both from a research and practitioner perspective and provide a research and application agenda for further work in this area. Finally, we make a contribution in the methodology underlying the preparation of review articles by means of involving the forecasting community in the process of deciding both the content and structure of this paper."
}
@INPROCEEDINGS{Taieb:2017,
  author = {Souhaib Ben Taieb and James W Taylor and Rob J Hyndman},
  year = 2017,
  title = {{Coherent Probabilistic Forecasts for Hierarchical Time Series}},
  booktitle = {Proceedings of the 34th International Conference on Machine Learning},
  pages = {3348-3357},
  publisher = {PMLR},
}
@article{Taieb2020,
author = {Souhaib Ben Taieb and James W. Taylor and Rob J. Hyndman},
title = {Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data},
journal = {Journal of the American Statistical Association},
volume = {0},
number = {0},
pages = {1-17},
year  = {2020},
publisher = {Taylor & Francis},
doi = {10.1080/01621459.2020.1736081},
IGNOREURL = {https://www.tandfonline.com/doi/full/10.1080/01621459.2020.1736081}
}
@article{Taylor2007,
title = "Forecasting daily supermarket sales using exponentially weighted quantile regression",
journal = "European Journal of Operational Research",
volume = "178",
number = "1",
pages = "154 - 167",
year = "2007",
issn = "0377-2217",
doi = "10.1016/j.ejor.2006.02.006",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0377221706000737",
author = "James W. Taylor",
keywords = "Forecasting, Exponential smoothing, Quantile regression, Interval forecasting, Robust point forecasting",
abstract = "Inventory control systems typically require the frequent updating of forecasts for many different products. In addition to point predictions, interval forecasts are needed to set appropriate levels of safety stock. The series considered in this paper are characterised by high volatility and skewness, which are both time-varying. These features motivate the consideration of forecasting methods that are robust with regard to distributional assumptions. The widespread use of exponential smoothing for point forecasting in inventory control motivates the development of the approach for interval forecasting. In this paper, we construct interval forecasts from quantile predictions generated using exponentially weighted quantile regression. The approach amounts to exponential smoothing of the cumulative distribution function, and can be viewed as an extension of generalised exponential smoothing to quantile forecasting. Empirical results are encouraging, with improvements over traditional methods being particularly apparent when the approach is used as the basis for robust point forecasting."
}
@ARTICLE{taylor:ProcLondon1921,
  author = {Taylor, G. I.},
  year = 1921,
  title = {{Diffusion by Continuous Movements}},
  journal = {Proc. London Math. Soc.},
  volume = 2,
  number = 20,
  pages = 196
}
@BOOK{tennekes:book99,
  author = {H. Tennekes and J. L. Lumley},
  year = 1999,
  title = {{A First Course in Turbulence}},
  note = {ISBN - 0262200198},
  publisher = {MIT Press, Cambridge, MA}
}
@article{Teunter2011,
title = "Intermittent demand: Linking forecasting to inventory obsolescence",
journal = "European Journal of Operational Research",
volume = "214",
number = "3",
pages = "606 - 615",
year = "2011",
issn = "0377-2217",
doi = "10.1016/j.ejor.2011.05.018",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0377221711004437",
author = "Ruud H. Teunter and Aris A. Syntetos and M. {Zied Babai}",
keywords = "Forecasting, Intermittent demand, Inventory control, Obsolescence",
abstract = "The standard method to forecast intermittent demand is that by Croston. This method is available in ERP-type solutions such as SAP and specialised forecasting software packages (e.g. Forecast Pro), and often applied in practice. It uses exponential smoothing to separately update the estimated demand size and demand interval whenever a positive demand occurs, and their ratio provides the forecast of demand per period. The Croston method has two important disadvantages. First and foremost, not updating after (many) periods with zero demand renders the method unsuitable for dealing with obsolescence issues. Second, the method is positively biased and this is true for all points in time (i.e. considering the forecasts made at an arbitrary time period) and issue points only (i.e. considering the forecasts following a positive demand occurrence only). The second issue has been addressed in the literature by the proposal of an estimator (Syntetos–Boylan Approximation, SBA) that is approximately unbiased. In this paper, we propose a new method that overcomes both these shortcomings while not adding complexity. Different from the Croston method, the new method is unbiased (for all points in time) and it updates the demand probability instead of the demand interval, doing so in every period. The comparative merits of the new estimator are assessed by means of an extensive simulation experiment. The results indicate its superior performance and enable insights to be gained into the linkage between demand forecasting and obsolescence."
}
@misc{TensorFlow2016,
doi = {10.48550/arxiv.1603.04467},
IGNOREurl = {https://arxiv.org/abs/1603.04467},
author = {Abadi, Martín and Agarwal, Ashish and Barham, Paul and Brevdo, Eugene and Chen, Zhifeng and Citro, Craig and Corrado, Greg S. and Davis, Andy and Dean, Jeffrey and Devin, Matthieu and Ghemawat, Sanjay and Goodfellow, Ian and Harp, Andrew and Irving, Geoffrey and Isard, Michael and Jia, Yangqing and Jozefowicz, Rafal and Kaiser, Lukasz and Kudlur, Manjunath and Levenberg, Josh and Mane, Dan and Monga, Rajat and Moore, Sherry and Murray, Derek and Olah, Chris and Schuster, Mike and Shlens, Jonathon and Steiner, Benoit and Sutskever, Ilya and Talwar, Kunal and Tucker, Paul and Vanhoucke, Vincent and Vasudevan, Vijay and Viegas, Fernanda and Vinyals, Oriol and Warden, Pete and Wattenberg, Martin and Wicke, Martin and Yu, Yuan and Zheng, Xiaoqiang},
keywords = {Distributed, Parallel, and Cluster Computing (cs.DC), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems},
publisher = {arXiv},
year = {2016},
copyright = {arXiv.org perpetual, non-exclusive license}
}
@book{Theil74,
title={Linear Aggregation of Economic Relations},
author={Theil, H.},
isbn={9780720431926},
lccn={73088230},
series={Contributions to economic analysis},
year={1974},
publisher={North-Holland}
}
@article{THEODOROU20221500,
title = {Exploring the representativeness of the {M5} competition data},
journal = {International Journal of Forecasting},
volume = {38},
number = {4},
pages = {1500-1506},
year = {2022},
note = {Special Issue: M5 competition},
issn = {0169-2070},
doi = {https://doi.org/10.1016/j.ijforecast.2021.07.006},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0169207021001175},
author = {Evangelos Theodorou and Shengjie Wang and Yanfei Kang and Evangelos Spiliotis and Spyros Makridakis and Vassilios Assimakopoulos},
keywords = {Forecasting competitions, M5, Time series visualization, Time series features, Retail sales forecasting},
abstract = {The main objective of the M5 competition, which focused on forecasting the hierarchical unit sales of Walmart, was to evaluate the accuracy and uncertainty of forecasting methods in the field to identify best practices and highlight their practical implications. However, can the findings of the M5 competition be generalized and exploited by retail firms to better support their decisions and operation? This depends on the extent to which M5 data is sufficiently similar to unit sales data of retailers operating in different regions selling different product types and considering different marketing strategies. To answer this question, we analyze the characteristics of the M5 time series and compare them with those of two grocery retailers, namely Corporación Favorita and a major Greek supermarket chain, using feature spaces. Our results suggest only minor discrepancies between the examined data sets, supporting the representativeness of the M5 data.}
}
@ARTICLE{thorpe:jfm82,
  author = {Thorpe, S. A.},
  year = 1982,
  title = {{On the Layers Produced by Rapidly Oscillating a Vertical Grid in a Uniformly Stratified Fluid}},
  journal = {Journal of Fluid Mechanics},
  volume = 124,
  pages = {391-409}
}
@article{Tian2021,
title = {{The impact of weather on consumer behavior and retail performance: Evidence from a convenience store chain in China}},
journal = {Journal of Retailing and Consumer Services},
volume = {62},
pages = {102583},
year = {2021},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2021.102583},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698921001491},
author = {Xin Tian and Shasha Cao and Yan Song},
keywords = {Retail, Weather, Consumer behavior, Store performance},
abstract = {Despite extensive use of weather data to adjust replenishment and inventory strategies in the retail industry, these companies do clearly know the effect of weather on consumer behavior and retail performance. How does weather affect consumers' purchasing behavior and thus retail performance? We study empirically these questions by analyzing more than 6 million transactions made by more than 1.62 million unique consumers at 146 convenience stores in a convenience store chain in China. We choose sun, rain, temperature, and air quality index as the main weather variables. We use the average number of items per order and the average price of each item in each order as indicators of consumer behavior, and use store daily sales as an indicator of retail performance. We found that under rainy weather, people will buy more products with higher item prices in one order. When temperature rises, people will buy fewer products with a lower item price in one order. In addition, sunny weather and rainy weather have a positive impact on daily sales than cloudy weather. Air quality has a negative impact on daily sales, while temperature has a positive impact on sales. Finally, we study the impact of weather on different product categories. We find that the results depend on the product category characteristics.}
}
@article{trapero2013analysis,
  title={Analysis of judgmental adjustments in the presence of promotions},
  author={Trapero, Juan R and Pedregal, Diego J and Fildes, Robert and Kourentzes, Nikolaos},
  journal={International Journal of Forecasting},
  volume={29},
  number={2},
  pages={234--243},
  year={2013},
  publisher={Elsevier},
  doi = {10.1016/j.ijforecast.2012.10.002}
}
@article{Trapero2015,
title = "On the identification of sales forecasting models in the presence of promotions",
journal = "Journal of the Operational Research Society",
volume = "66",
number = "2",
pages = "299-307",
year = "2015",
doi = "10.1057/jors.2013.174",
IGNOREurl = "https://link.springer.com/article/10.1057/jors.2013.174",
author = "Juan R. Trapero and Nikolaos Kourentzes and Robert Fildes",
abstract = "Shorter product life cycles and aggressive marketing, among other factors, have increased the complexity of sales forecasting. Forecasts are often produced using a Forecasting Support System that integrates univariate statistical forecasting with managerial judgment. Forecasting sales under promotional activity is one of the main reasons to use expert judgment. Alternatively, one can replace expert adjustments by regression models whose exogenous inputs are promotion features (price, display, etc). However, these regression models may have large dimensionality as well as multicollinearity issues. We propose a novel promotional model that overcomes these limitations. It combines Principal Component Analysis to reduce the dimensionality of the problem and automatically identifies the demand dynamics. For items with limited history, the proposed model is capable of providing promotional forecasts by selectively pooling information across established products. The performance of the model is compared against forecasts provided by experts and statistical benchmarks, on weekly data; outperforming both substantially."
}
@Manual{tsutils19,
title = {{tsutils}: Time Series Exploration, Modelling and Forecasting.},
author = {Nikolaos Kourentzes and Ivan Svetunkov and Oliver Schaer},
year = {2020},
note = {R package version 0.9.2},
url = {https://CRAN.R-project.org/package=tsutils}
}
@Manual{tsutils,
    title = {tsutils: Time Series Exploration, Modelling and Forecasting. {R} package version 0.9.2},
    author = {Nikolaos Kourentzes},
    year = {2020},
    url = {https://CRAN.R-project.org/package=tsutils},
}
@article{VALLESPEREZ2022,
title = {Approaching sales forecasting using recurrent neural networks and transformers},
journal = {Expert Systems with Applications},
volume = {201},
pages = {116993},
year = {2022},
issn = {0957-4174},
doi = {10.1016/j.eswa.2022.116993},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0957417422004146},
author = {Iván Vallés-Pérez and Emilio Soria-Olivas and Marcelino Martínez-Sober and Antonio J. Serrano-López and Juan Gómez-Sanchís and Fernando Mateo},
keywords = {Sales forecast, Supply chain, Deep learning, Transformer, Sequence to sequence},
abstract = {Accurate and fast demand forecast is one of the hot topics in supply chain for enabling the precise execution of the corresponding downstream processes (inbound and outbound planning, inventory placement, network planning, etc.). We develop three alternatives to tackle the problem of forecasting the customer sales at day/store/item level using deep learning techniques and the Corporación Favorita data set, published as part of a Kaggle competition. Our empirical results show how good performance can be achieved by using a simple sequence to sequence architecture with minimal data preprocessing effort. Additionally, we describe a training trick for making the model more time independent and hence improving generalization over time. The proposed solution achieves a RMSLE of around 0.54, which is competitive with other more specific solutions to the problem proposed in the Kaggle competition}
}
@article{Veiga2016,
title = {Demand forecasting based on natural computing approaches applied to the foodstuff retail segment},
journal = {Journal of Retailing and Consumer Services},
volume = {31},
pages = {174-181},
year = {2016},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2016.03.008},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698916301254},
author = {Claudimar Pereira da Veiga and Cássia Rita Pereira da Veiga and Weslly Puchalski and Leandro dos Santos Coelho and Ubiratã Tortato},
keywords = {Takagi-Sugeno Fuzzy System, Wavelets neural network, Strategy, Foodstuff retail, Fill rate},
abstract = {The purpose of this paper is to compare the accuracy of demand forecasting between two classical linear forecasting models (Autoregressive and Integrated Moving Average -ARIMA and Holt-Winter) and two nonlinear forecasting models based on natural computing approaches (Wavelets Neural Networks - WNN and Takagi-Sugeno Fuzzy System - TS), all applied to the aggregated retail sales of three groups of perishable food products from 2005 to 2013. Moreover, this paper evaluates the impact of demand forecasting accuracy on the demand satisfaction rate and on the overall economic performance of retail business operations. The most accurate model, WNN, had a demand satisfaction rate of 98.27\% for Group A, 98.83\% for Group B and 98.80\% for Group C. WNN estimated a loss of revenue of R\$1329.14 million/year with a minimum loss of 166 tons/year, which means that the results of WNN are 37.67\% more efficient than the TS, 57.49\% higher than the ARIMA and 76.79\% higher than HW. This paper presents three main contributions: (i) it examines a question not evaluated in the literature on demand forecasting based on natural computing approaches in the foodstuff retail segment that generates better practical results, (ii) it proposes that a single forecasting model could be applied to different product groups and serves the organization as a whole with a good relationship between the cost and the benefit of the process and (iii) like previous studies, it proves that demand forecasting plays an important role and can generate a competitive advantage for the organization to be incorporated into its strategy.}
}
@article{Verstraete2019,
title = {A data-driven framework for predicting weather impact on high-volume low-margin retail products},
journal = {Journal of Retailing and Consumer Services},
volume = {48},
pages = {169-177},
year = {2019},
issn = {0969-6989},
doi = {10.1016/j.jretconser.2019.02.019},
IGNOREurl = {https://www.sciencedirect.com/science/article/pii/S0969698918307203},
author = {Gylian Verstraete and El-Houssaine Aghezzaf and Bram Desmet},
keywords = {Sales forecasting, Machine learning, Weather},
abstract = {Accurate demand forecasting is of critical importance to retail companies operating in high-volume low-margin industries. Inaccuracies in the forecasts lead either to stock-outs or to excess inventories, resulting in either lost sales or higher working capital, and for both cases in extra unnecessary costs. Prediction accuracy is essential to retail companies having a part of their product portfolio manufactured in low-cost countries and requiring long delivery times. It is rather vital when the demand for these goods is strongly weather dependent. The combination of long delivery times and weather dependence creates a business challenge, as the availability period of accurate weather information is much shorter than the lead time. In this paper we propose a methodology that handles the impact of both the short-term (with available weather data) and the long-term weather uncertainty on the forecast. For the former, the proposed framework is capable of automatically selecting the best prediction model. For latter, the framework fits a distribution on simulated and aggregated sales using the short-term regression model with historical weather data. This framework has been tested on a company's sales data and is proven to satisfactorily address the challenges that the company is facing.}
}
@article{Villegas2018,
title = "Supply chain decision support systems based on a novel hierarchical forecasting approach",
journal = "Decision Support Systems",
volume = "114",
pages = "29 - 36",
year = "2018",
issn = "0167-9236",
doi = "10.1016/j.dss.2018.08.003",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0167923618301246",
author = "Marco A. Villegas and Diego J. Pedregal",
keywords = "Forecasting, Hierarchical forecasting, Reconciliation, State Space, Decision support system",
abstract = "Time series forecasting plays an important role in many decision support systems, also in those related to the management of supply chains. Forecast accuracy is, therefore, essential to optimise the efficiency of any supply chain. One aspect that is often overlooked is the fact that sales of many products within an organization are assembled as complex hierarchies with different levels of aggregation. Very often forecasts are produced regardless of such structure, though forecasting accuracy may be improved by taking it into account. In this paper, an approach for hierarchical time series forecasting based on State Space modelling is proposed. Previous developments provide solutions to the hierarchical forecasting problem by algebra manipulations based on forecasts produced by independent models for each time series involved in the hierarchy. The solutions produce optimal reconciled forecasts for each individual forecast horizon, but the link along time that is implied by the dynamics of the models is completely ignored. Therefore, the novel approach in this paper improves upon past research at least in two key points. Firstly, the algebra is already encoded in the State Space system and the Kalman Filter algorithm, giving an elegant and clean solution to the problem. Secondly, the State Space approach is optimal both across the hierarchy, as expected, but also along time, something missing in past developments. The approach is assessed by comparing its forecasting performance to the existing methods, through simulations and using real data of a Spanish grocery retailer."
}
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  author = {{VISJET}},
  year = 2003,
  title = {{VISJET - Computer Ocean Outfall Modelling System}},
  note = {{The University of Hong Kong}},
  url = {http://www.aoe-water.hku.hk/visjet/}
}
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  author = {Frick, W. E. and Roberts, P. J. W. and Davis, L. R. and Keyes, J. and Baumgartner, D. J. and George, K. P.},
  year = 2001,
  title = {{Dilution Models for Effluent Discharges, 4th Edition (Visual Plumes) Draft}},
  address = {Athens, Georgia},
  organization = {{U. S. Environmental Protection Agency}, Environmental Research Division, NERL, Standards and Applied Science Division, Office of Science and Technology}
}
@article{vanDonselaar2016,
title = "Analysis and forecasting of demand during promotions for perishable items",
journal = "International Journal of Production Economics",
volume = "172",
pages = "65-75",
year = "2016",
issn = "0925-5273",
doi = "10.1016/j.ijpe.2015.10.022",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S092552731500403X",
author = "K.H. {van Donselaar} and J. Peters and A. {de Jong} and R.A.C.M. Broekmeulen",
keywords = "Perishable, Promotion, Price discount, Retail, Demand forecasting, Threshold, Saturation",
abstract = "This study examines promotions for perishable products in a retail environment. We analyze the impact of relative price discounts on product sales during a promotion and shed light on how to build models to forecast promotional demand for perishable products. Preliminary analyses, based on regression models and a large dataset from a retailer, do not reveal conclusive evidence for the presence of threshold and/or saturation levels for price discounts for perishable products. A potential explanation comes from the observation that, although products like desserts on average allow 1,5 weeks time-to-consume, their sales during promotions on average are equal to 14 weeks of regular sales. This suggests that the success of a promotion is not so much determined by the restriction to stockpile (due to the short time-to-consume) but by the emergence of substitution effects (consumers switching between different products of the same category). We develop and test different models to forecast the demand during a promotion, including a moving average forecast and several regression models. Within the class of regression models we find that modeling threshold and saturation effects leads to worse forecasting performance than modeling price reductions linearly or quadratically. The largest improvements in forecast accuracy are gained by distinguishing between routine and non-routine product categories. Routine categories with routine demand processes and a large number of observations perform best when applying a regression based on direct observations of the product category, whilst non-routine categories benefit from a regression which also uses observations from other product categories."
}
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  author = {USEPA},
  year = 2003,
  title = {{VISUAL PLUMES}},
  url = {http://www.cee.odu.edu/model/visual_plume.php}
}
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  editor = {Springer},
  year = 2003,
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@InProceedings{Wang2019,
title = {Deep Factors for Forecasting},
author = {Wang, Yuyang and Smola, Alex and Maddix, Danielle and Gasthaus, Jan and Foster, Dean and Januschowski, Tim},
booktitle = {36th International Conference on Machine Learning},
pages = {6607--6617},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
month = {09--15 Jun},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v97/wang19k/wang19k.pdf},
IGNOREurl = {https://proceedings.mlr.press/v97/wang19k.html},
abstract = {Producing probabilistic forecasts for large collections of similar and/or dependent time series is a practically highly relevant, yet challenging task. Classical time series models fail to capture complex patterns in the data and multivariate techniques struggle to scale to large problem sizes, but their reliance on strong structural assumptions makes them data-efficient and allows them to provide estimates of uncertainty. The converse is true for models based on deep neural networks, which can learn complex patterns and dependencies given enough data. In this paper, we propose a hybrid model that incorporates the benefits of both approaches. Our new method is data-driven and scalable via a latent, global, deep component. It also handles uncertainty through a local classical model. We provide both theoretical and empirical evidence for the soundness of our approach through a necessary and sufficient decomposition of exchangeable time series into a global and a local part and extensive experiments. Our experiments demonstrate the advantages of our model both in term of data efficiency and computational complexity.}
}
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  title={Characteristic-based clustering for time series data},
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  publisher={Springer}
}
@article{wang2007regression,
  title={Regression coefficient and autoregressive order shrinkage and selection via the lasso},
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  year={2007},
  publisher={Wiley Online Library}
}
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  editor = {ASCE},
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  title = {{Geostatistics for environmental scientists, 2nd ed.}},
  note = {{ISBN-13: 978-0-470-02858-2(HB)}},
}
@ARTICLE{webster:eif01a,
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  title = {{Simultaneous DPTV/PLIF Measurements of a Turbulent Jet}},
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  note = {Springer-Verlag 2001}
}
@ARTICLE{webster:eif01b,
  author = {D. R. Webster and Y. Liu},
  year = 2001,
  title = {{Velocity Measurements of Turbulence Collapse in a Linearly Stratified Jet}},
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  note = {Springer-Verlag 2001}
}
@ARTICLE{webster:limnology01a,
  author = {D. R. Webster},
  year = 2001,
  title = {{Chemosensory Guidance Cues in a Turbulent Chemical Odor Plume}},
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  number = 5,
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  note = {{by the American Society of Limnology and Oceanograhy, Inc.}}
}
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  author = {D. R. Webster and S. Rahman and L. P. Dasi},
  year = 2001,
  title = {{On the Usefulness of Bilateral Comparison to Tracking Turbulent Chemical Odor Plumes}},
  journal = {Limnology and Oceanography},
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  note = {by the Americal Society of Limnology and Oceanography, Inc.}
}
@BOOK{Wei05,
  author = {W. S. Wei},
  publisher = {Addison Wesley, 2nd edition},
  year = 2005,
  title = {{Time series analysis: univariate and multivariate methods}}
}
@ARTICLE{weissburg:efm02,
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  year = 2002,
  title = {{A Multidisciplinary Study of Spatial and Temporal Scales Containing Information in Turbulent Chemical Plume Tracking}},
  journal = {Environmental Fluid Mechanics},
  volume = 2,
  pages = {65-94}
}
@article{West06,
  author = {K. D. West},
  title = {Forecast evaluation},
  journal = {Handbook of Economic Forecasting},
  volume = {1},
  pages = {99-134},
  year = {2006}
}
@article{Wickramasuriya2019,
author = {Shanika L. Wickramasuriya and George Athanasopoulos and Rob J Hyndman},
title = {Optimal forecast reconciliation for hierarchical and grouped time series through trace minimization},
journal = {Journal of the American Statistical Association},
volume = {114},
number = {526},
pages = {804--19},
year = {2019},
doi = {10.1080/01621459.2018.1448825},
IGNOREurl = {https://www.tandfonline.com/doi/full/10.1080/01621459.2018.1448825}
}
@article{Widiarta09,
title = "Forecasting aggregate demand: An analytical evaluation of top-down versus bottom-up forecasting in a production planning framework",
journal = "International Journal of Production Economics",
volume = "118",
number = "1",
pages = "87 - 94",
year = "2009",
issn = "0925-5273",
doi = "10.1016/j.ijpe.2008.08.013",
IGNOREurl = "10.1016/j.ijpe.2008.08.013",
author = "Handik Widiarta and S. Viswanathan and Rajesh Piplani",
note = {doi:10.1016/j.ijpe.2008.08.013}
}
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  title = {{Performance Metrics for Oceanographic Surveys With Autonomous Underwater Vehicles}},
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  author = {L.D. Winiarski and W.E. Frick},
  year = 1976,
  title = {{Cooling Tower Plume Model}},
  note = {{EPA-600/3-76-100}},
  organization = {{CERL, USEPA, Corvallis, OR.}}
}
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  author = {S. F. Witt and H. Song and P. Louvieris},
  title = {Statistical testing in forecasting model selection},
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  year = {2003}
}
@TECHREPORT{wong:85,
  author = {Derek R. Wong},
  year = 1985,
  title = {{Buoyant Jet Entrainment in Stratified Fluids}},
  institution = {Department of Civil Engineering, University of Michigan at Ann Arbor},
  type = {Report {UMCE} 85-9}
}
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  author = {Derek R. Wong and Steven J. Wright},
  year = 1988,
  title = {{Submerged Turbulent Jets in Stagnant Linearly Stratified Fluids}},
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}
@article{Wong10,
  author = {W. K. Wong and Z. X. Guo},
  title = {A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm},
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  pages = {614-624},
  year = {2010}
}
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}
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}
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  author = {Steven J. Wright and Roger B. Wallace},
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}
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           Rober B. Wallace},
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}
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          California}},
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}
@article{Yagli2019,
title = "Reconciling solar forecasts: Sequential reconciliation",
journal = "Solar Energy",
volume = "179",
pages = "391 - 397",
year = "2019",
issn = "0038-092X",
doi = "10.1016/j.solener.2018.12.075",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0038092X18312726",
author = "Gokhan Mert Yagli and Dazhi Yang and Dipti Srinivasan",
keywords = "Forecast reconciliation, Operational forecasting, Numerical weather prediction",
abstract = "When forecasting hierarchical photovoltaic (PV) power generation in a region and/or over several forecast horizons, reconciliation is needed to ensure the lower-level forecasts add up exactly to the upper-level forecasts. Previously in “Reconciling solar forecasts: Geographical hierarchy” [Sol. Energy 146 (2017) 276–286] and “Reconciling solar forecasts: Temporal hierarchy” [Sol. Energy 158 (2017) 332–346], forecast reconciliation has been demonstrated for geographical and temporal hierarchies, separately. This article follows such frameworks and extends the reconciliation to spatio-temporal cases. More specifically, sequential reconciliation is used for operational day-ahead forecasting of 318 PV systems in California. It is shown that by using sequential reconciliation, aggregate-consistent forecasts can be obtained across both the geographical and temporal hierarchies. In addition, the forecast accuracy can be further improved from that of the single-hierarchy cases."
}
@article{Yang2017,
title = "Reconciling solar forecasts: Geographical hierarchy",
journal = "Solar Energy",
volume = "146",
pages = "276 - 286",
year = "2017",
issn = "0038-092X",
doi = "10.1016/j.solener.2017.02.010",
IGNOREurl = "http://www.sciencedirect.com/science/article/pii/S0038092X17301020",
author = "Dazhi Yang and Hao Quan and Vahid R. Disfani and Licheng Liu",
keywords = "Forecasting, Hierarchical reconciliation, Numerical weather prediction, Spatio-temporal forecasting",
abstract = "To integrate solar energy into the electricity grid reliably and efficiently, different forecasting techniques are used to produce forecasts at various forecast horizons using data of different levels of aggregation. For example, power generated by distributed photovoltaics (PV) can be disaggregated in a geographical hierarchy into transmission zones, distribution nodes, PV plants, subsystems and inverters; forecasts are required at all of these levels to facilitate different power system operations and power plant management. Due to the different information sets visible to different players in the hierarchy, such as PV plant owners and independent system operators, the forecasts produced at different aggregation levels are often not optimal. Furthermore, these forecasts are aggregate inconsistent, i.e., forecasts made using data collected at lower levels do not add up exactly to the forecasts made using higher level data. In this paper, the state-of-the-art forecast reconciliation techniques are explored. By minimizing the trace of forecast error covariance matrix, the base forecasts obtained across the hierarchy can be optimally reconciled. The reconciled forecasts not only aggregate consistently, and thus lead to collaborative decision making, but also improve the base forecasts at each level significantly. The merit of the reconciliation framework goes to the fact that it does not require any additional information other than those base forecasts. Furthermore, forecast improvements are independent of the base forecasting methods. In other words, once the base forecasts are generated with our favorite methods, the reconciliation will most likely improve those forecasts further. The reconciliation techniques can be applied to a variety of hierarchies with different forecast horizons. The empirical part of this work considers two examples, namely, an NWP-based day-ahead forecast reconciliation over 318 simulated PV plants in the state of California, and a spatio-temporal statistical 1-min-ahead forecast reconciliation over 17 irradiance sensors on the Oahu Island, Hawaii. It is shown that reconciliation benefits both the gird operators and PV system owners by bringing more accurate forecasts, and thus motivates information sharing in an electricity grid."
}
@ARTICLE{Zhang2018,
author={Y. {Zhang} and J. {Dong}},
journal={IEEE Transactions on Power Systems}, 
title={Least Squares-based Optimal Reconciliation Method for Hierarchical Forecasts of Wind Power Generation}, 
year={2018},
abstract={Wind power generation is hierarchically organized and can be aggregated/disaggregated based on geography and electrical network structure. Forecasts are required at all levels of such a hierarchy for different operational requirements, which are also known as “hierarchical forecasts”. In this paper, we propose a new method to produce hierarchical wind power forecasts which performs better than the conventional top-down or bottom-up method. At first, wind power generation at all levels of the hierarchy is independently forecasted using state-of-the-art techniques. Then, a least squares regression model is used to optimally reconcile these base forecasts. Adjusted forecasts are as close as possible to base forecasts but also satisfy the requirement of the aggregate consistency (i.e., the lower-level forecasts add up exactly to the higher-level forecasts). Case studies validate the effectiveness of the proposed approach for very short-term wind power forecasts. The accuracy improvement of reconciled forecasts over benchmarks is confirmed at all levels of the hierarchy, regardless of forecast horizons and hierarchical structures.},
keywords={Wind forecasting;Wind power generation;Time series analysis;Forecasting;Aggregates;Wind farms;Indexes;Bottom-up forecasts;combining forecasts;hier-archical forecasting;least squares regression;top-down forecasts;very short-term forecasting},
doi={10.1109/TPWRS.2018.2868175},
IGNOREurl={https://ieeexplore.ieee.org/document/8453006},
ISSN={1558-0679}
}
@article{Zellner00,
author = {Zellner, Arnold and Tobias, Justin},
title = {A note on aggregation, disaggregation and forecasting performance},
journal = {Journal of Forecasting},
volume = {19},
number = {5},
pages = {457-465},
doi = {10.1002/1099-131X(200009)19:5<457::AID-FOR761>3.0.CO;2-6},
year = {2000},
note = {doi:10.1002/1099-131X(200009)19:5<457::AID-FOR761>3.0.CO;2-6},
IGNOREurl = {10.1002/1099-131X(200009)19:5<457::AID-FOR761>3.0.CO;2-6}
}
@ARTICLE{Zhang:LSCM01,
  author = {Xiong Zhang and Xiao-Hu Liu and Kang-Zu Song and Ming-Wan Lu},
  year = 2001,
  title = {{Least-Squares Collocation Meshless Method}},
  journal = {{International Journal for Numerical Methods in Engineering}},
  volume = 51,
  pages = {1089-1100}
}
@article{Zhang05,
  author = {G. Zhang and M. Qi},
  title = {Neural network forecasting for seasonal and trend time series},
  journal = {European Journal of Operational Research},
  volume = {160},
  pages = {501-514},
  year = {2005}
}
@article{Zhao01,
  author = {X. Zhao and J. Xie and R. S. M. Lau},
  title = {Improving the supply chain performance: Use of forecasting models versus early order commitments},
  journal = {International Journal of Production Research},
  volume = {39},
  number = {17},
  pages = {3923-3939},
  year = {2001}
}
@article{Zhao2017,
  title={Sales Forecast in E-commerce using Convolutional Neural Network},
  author={Kui Zhao and C. Wang},
  journal={ArXiv},
  year={2017},
  volume={abs/1708.07946}
}
@article{Zhou2022,
author = {He Zhou and Wei Qian and Yi Yang},
title = {Tweedie gradient boosting for extremely unbalanced zero-inflated data},
journal = {Communications in Statistics - Simulation and Computation},
volume = {51},
number = {9},
pages = {5507-5529},
year  = {2022},
publisher = {Taylor & Francis},
doi = {10.1080/03610918.2020.1772302}
}
@article{Zotteri01,
  author = {Giulio Zotteri and Matteo Kalchschmidt and Federico Caniato},
  title = {The impact of aggregation level on forecasting performance},
  journal = {International Journal of Production Economics},
  volume = {93–94},
  number = {8},
  pages = {479-491},
  year = {2005}
}
@article{Zvonkin1970,
doi = {10.1070/RM1970v025n06ABEH001269},
url = {https://dx.doi.org/10.1070/RM1970v025n06ABEH001269},
year = {1970},
month = {dec},
publisher = {},
volume = {25},
number = {6},
pages = {83},
author = {A K Zvonkin and  L A Levin},
title = {THE COMPLEXITY OF FINITE OBJECTS AND THE DEVELOPMENT OF THE CONCEPTS OF INFORMATION AND RANDOMNESS BY MEANS OF THE THEORY OF ALGORITHMS},
journal = {Russian Mathematical Surveys},
abstract = {In 1964 Kolmogorov introduced the concept of the complexity of a finite object (for instance, the words in a certain alphabet). He defined complexity as the minimum number of binary signs containing all the information about a given object that are sufficient for its recovery (decoding). This definition depends essentially on the method of decoding. However, by means of the general theory of algorithms, Kolmogorov was able to give an invariant (universal) definition of complexity. Related concepts were investigated by Solomonoff (U.S.A.) and Markov. Using the concept of complexity, Kolmogorov gave definitions of the quantity of information in finite objects and of the concept of a random sequence (which was then defined more precisely by Martin-Löf). Afterwards, this circle of questions developed rapidly. In particular, an interesting development took place of the ideas of Markov on the application of the concept of complexity to the study of quantitative questions in the theory of algorithms. The present article is a survey of the fundamental results connected with the brief remarks above.}
}
