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Massive feature extraction for explaining and foretelling hydroclimatic time series forecastability at the global scale

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Geoscience Frontiers 2022年第3期13卷 79-99页

作者： Georgia Papacharalampous Hristos Tyralis Ilias G.Pechlivanidis Salvatore Grimaldi Elena Volpi Department of Engineering Roma Tre UniversityVia V.Volterra 6200146 RomeItaly Department of Water Resources and Environmental Engineering School of Civil EngineeringNational Technical University of AthensHeroon Polytechneiou 515780 ZographouGreece Hellenic Air Force General Staff Hellenic Air ForceMesogion Avenue 227-23115561 CholargosGreece Swedish Meteorological and Hydrological Institute 60176 NorrköpingSweden Department for Innovation in Biological Agro-food and Forest SystemsUniversity of TusciaViterboItaly Department of Mechanical and Aerospace Engineering Tandon School of EngineeringNew York UniversityBrooklynNY10003United States

statistical analyses and descriptive characterizations are sometimes assumed to be offering information on time series *** the scientific interest suggested by such assumptions,the relationships between descriptive time series features(e.g.,temporal dependence,entropy,seasonality,trend and linearity features)and actual time series forecastability(quantified by issuing and assessing forecasts for the past)are scarcely studied and quantified in the *** this work,we aim to fill in this gap by investigating such relationships,and the way that they can be exploited for understanding hydroclimatic forecastability and its *** this end,we follow a systematic framework bringing together a variety of–mostly new for hydrology–concepts and methods,including 57 descriptive features and nine seasonal time series forecasting methods(i.e.,one simple,five exponential smoothing,two state space and one automated autoregressive fractionally integrated moving average methods).We apply this framework to three global datasets originating from the larger Global Historical Climatology Network(GHCN)and Global Streamflow Indices and Metadata(GSIM)*** these datasets comprise over 13,000 monthly temperature,precipitation and river flow time series from several continents and hydroclimatic regimes,they allow us to provide trustable characterizations and interpretations of 12-month ahead hydroclimatic forecastability at the global *** first find that the exponential smoothing and state space methods for time series forecasting are rather equally efficient in identifying an upper limit of this forecastability in terms of Nash-Sutcliffe efficiency,while the simple method is shown to be mostly useful in identifying its lower *** then demonstrate that the assessed forecastability is strongly related to several descriptive features,including seasonality,entropy,(partial)autocorrelation,stability,(non)linearity,spikiness and heterogeneity features

关键词： Exponential smoothing Predictability statistical hydrology Time series analysis Time series clustering Time series forecasting

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