Cross-Learning with Short Seasonal Time Series

Huijing Chen, John Boylan and Ivan Svetunkov

Foresight: The International Journal of Applied Forecasting, 2023, issue 70, 17-23

Abstract: Since its introduction by R. G. Brown over 60 years ago, exponential smoothing, in its various flavors, has been a go-to model for many forecasting professionals. Thanks to its solid performance across 40 years of M competitions, exponential smoothing has earned a secure place in the forecaster's toolbox. The familiar Error-Trend-Seasonality (ETS) taxonomy by Hyndman and colleagues helps define how components of a time series interact with each other, and this new research by Chen, Boylan, and Svetunkov provides an enhanced taxonomy that can aid in cross-learning from similar time series with very short histories. Copyright International Institute of Forecasters, 2023

Date: 2023
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