Category: Default

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…

Authors:  Xiaoqian Wang, Yanfei Kang, Fotios Petropoulos and Feng Li Abstract:  Interval forecasts have significant advantages in providing uncertainty estimation to point forecasts, leading to the importance of providing prediction intervals (PIs) as well as point forecasts. In this paper, we propose a general feature-based time series forecasting framework, which is divided into “offline” and “online” parts.…