Ramos, P.; Oliveira, J.M. Robust Sales forecasting Using Deep Learning with Static and Dynamic Covariates. Appl. Syst. Innov.2023, 6, 85.
Ramos, P.; Oliveira, J.M. Robust Sales forecasting Using Deep Learning with Static and Dynamic Covariates. Appl. Syst. Innov. 2023, 6, 85.
Ramos, P.; Oliveira, J.M. Robust Sales forecasting Using Deep Learning with Static and Dynamic Covariates. Appl. Syst. Innov.2023, 6, 85.
Ramos, P.; Oliveira, J.M. Robust Sales forecasting Using Deep Learning with Static and Dynamic Covariates. Appl. Syst. Innov. 2023, 6, 85.
Abstract
Retailers must have accurate sales forecasts to operate their businesses efficiently and effectively and to remain competitive in the marketplace. Global forecasting models like RNNs can be a powerful tool for forecasting in retail settings, where multiple time series are often interrelated and influenced by a variety of external factors. By including covariates in a forecasting model, we can better capture the various factors that can influence sales in a retail setting. This can help improve the accuracy of our forecasts and enable better decision-making for inventory management, purchasing, and other operational decisions. In this study we investigate how the accuracy of global forecasting models is affected by the inclusion of different potential demand covariates. To ensure the significance of the study's findings, we used the M5 forecasting competition's openly accessible and well-established dataset. The results obtained from the DeepAR models, which were trained on various combinations of features including time, price, events, and IDs, suggest that individually only the features corresponding to IDs improve the baseline model. However, when all the features are used together, the best performance is achieved, indicating that the individual relevance of each feature is emphasized when the information is given jointly. Comparing the model with features to the model without features, there is an improvement of 1.76\% for MRMSSE and 6.47\% for MMASE.
Keywords
deep neural networks; time series forecasting; covariates; retailing
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
