A New Taxonomy for Vector Exponential Smoothing and Its Application to Seasonal Time Series

Authors: Ivan Svetunkov, Huijing Chen, John E. Boylan.

Journal: European Journal of Operational Research

Abstract: In short-term demand forecasting, it is often difficult to estimate seasonality accurately, owing to short data histories. However, companies usually have multiple products with similar seasonal demand patterns. A possible solution, in this case, is to use the components of several time series from a homogeneous family, thus estimating seasonal coefficients based on cross-sectional information. Motivated by this practical problem, we propose a new taxonomy of Parameters, Initial States and Components (PIC), which exploits homogeneous features of time series. We then apply this framework to vector exponential smoothing. We develop a model selection mechanism based on information criteria to select the appropriate PIC restrictions. We then conduct a simulation experiment and empirical analysis on retail data in order to assess the performance of point forecasts and prediction intervals of the models within this framework.

The link to the working paper.

DOI: 10.1016/j.ejor.2022.04.040

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