Article
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Preserved in Portico This version is not peer-reviewed
Comparison of Stochastic and Machine Learning Methods for Multi-Step Ahead Forecasting of Hydrological Processes
Version 1
: Received: 20 October 2017 / Approved: 20 October 2017 / Online: 20 October 2017 (03:18:02 CEST)
Version 2 : Received: 19 February 2018 / Approved: 20 February 2018 / Online: 20 February 2018 (13:32:47 CET)
Version 3 : Received: 1 January 2019 / Approved: 17 January 2019 / Online: 17 January 2019 (15:51:22 CET)
Version 2 : Received: 19 February 2018 / Approved: 20 February 2018 / Online: 20 February 2018 (13:32:47 CET)
Version 3 : Received: 1 January 2019 / Approved: 17 January 2019 / Online: 17 January 2019 (15:51:22 CET)
A peer-reviewed article of this Preprint also exists.
Papacharalampous, G., Tyralis, H. & Koutsoyiannis, D. Stoch Environ Res Risk Assess (2019). https://doi.org/10.1007/s00477-018-1638-6 Papacharalampous, G., Tyralis, H. & Koutsoyiannis, D. Stoch Environ Res Risk Assess (2019). https://doi.org/10.1007/s00477-018-1638-6
Abstract
We perform an extensive comparison between 11 stochastic to 9 machine learning methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments. Each of these experiments uses 2 000 time series generated by linear stationary stochastic processes. We conduct each simulation experiment twice; the first time using time series of 110 values and the second time using time series of 310 values. Additionally, we conduct 92 real-world case studies using mean monthly time series of streamflow and particularly focus on one of them to reinforce the findings and highlight important facts. We quantify the performance of the methods using 18 metrics. The results indicate that the machine learning methods do not differ dramatically from the stochastic, while none of the methods under comparison is uniformly better or worse than the rest. However, there are methods that are regularly better or worse than others according to specific metrics.
Supplementary and Associated Material
https://doi.org/10.17632/fjr8244m35.1: supplementary files
Keywords
multi-step ahead forecasting; neural networks; random forests; stochastic vs machine learning models; support vector machines; time series
Subject
Engineering, Civil Engineering
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.
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