Preprint Article Version 2 This version is not peer-reviewed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)

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

Journal reference: Stochastic Environmental Research and Risk Assessment 2019, 2019, n/a
DOI: 10.1007/s00477-018-1638-6

Abstract

Research within the field of hydrology often focuses on comparing stochastic to machine learning (ML) forecasting methods. The comparisons performed are all based on case studies, while an extensive study aiming to provide generalized results on the subject is missing. Herein, we compare 11 stochastic and 9 ML methods regarding their multi-step ahead forecasting properties by conducting 12 large-scale computational experiments based on simulations. 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 100 values and the second time using time series of 300 values. Additionally, we conduct a real-world experiment using 405 mean annual river discharge time series of 100 values. We quantify the performance of the methods using 18 metrics. The results indicate that stochastic and ML methods perform equally well.

Subject Areas

multi-step ahead forecasting; neural networks; random forests; stochastic vs machine learning models; support vector machines; time series

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