New paper: Déjà vu: forecasting with similarity

Authors:  Yanfei Kang, Evangelos Spiliotis, Fotios Petropoulos, Nikolaos Athiniotis, Feng Li, and Vassilios Assimakopoulos Accurate forecasts are vital for supporting the decisions of modern companies. To improve statistical forecasting performance, forecasters typically select the most appropriate model for each given time series. However, statistical models usually presume some data generation process, while making strong distributional assumptions about the errors. In… Continue reading New paper: Déjà vu: forecasting with similarity

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