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• W2766801343 endingPage "514" @default.
• W2766801343 startingPage "481" @default.
• W2766801343 abstract "Research within the field of hydrology often focuses on the statistical problem of comparing stochastic to machine learning (ML) forecasting methods. The performed comparisons are based on case studies, while a study providing large-scale 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 extensive computational experiments based on simulations. Each of these experiments uses 2000 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 forecasting performance of the methods using 18 metrics. The results indicate that stochastic and ML methods may produce equally useful forecasts." @default.
• W2766801343 created "2017-11-10" @default.
• W2766801343 creator A5003329348 @default.
• W2766801343 creator A5045992049 @default.
• W2766801343 creator A5080769137 @default.
• W2766801343 date "2019-01-01" @default.
• W2766801343 modified "2023-09-30" @default.
• W2766801343 title "Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes" @default.
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