One-step ahead forecasting of geophysical processes within a purely statistical framework

Georgia Papacharalampous, Hristos Tyralis & Demetris Koutsoyiannis
Abstract The simplest way to forecast geophysical processes, an engineering problem with a widely recognized challenging character, is the so-called “univariate time series forecasting” that can be implemented using stochastic or machine learning regression models within a purely statistical framework. Regression models are in general fast-implemented, in contrast to the computationally intensive Global Circulation Models, which constitute the most frequently used alternative for precipitation and temperature forecasting. For their simplicity and easy applicability, the former...
This data repository is not currently reporting usage information. For information on how your repository can submit usage information, please see our documentation.