Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes

@article{Papacharalampous2019ComparisonOS,
  title={Comparison of stochastic and machine learning methods for multi-step ahead forecasting of hydrological processes},
  author={Georgia Papacharalampous and Hristos Tyralis and Demetris Koutsoyiannis},
  journal={Stochastic Environmental Research and Risk Assessment},
  year={2019},
  volume={33},
  pages={481-514}
}
Research within the field of hydrology often focuses on the statistical problem of comparing stochastic to machine learning (ML) forecasting methods. [...] Key Method 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.Expand
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Forecasting of geophysical processes using stochastic and machine learning algorithms: Supplementary material
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An extensive comparison between four stochastic and two machine learning (ML) forecasting algorithms by conducting a multiple-case study of time series of total monthly precipitation and mean monthly temperature observed in Greece is performed. Expand
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Error Evolution in Multi-Step Ahead Streamflow Forecasting for the Operation of Hydropower Reservoirs
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