Svetunkov, Ivan and Chen, Huijing and Boylan, John E. (2023) A New Taxonomy for Vector Exponential Smoothing and Its Application to Seasonal Time Series. European Journal of Operational Research, 304 (3). pp. 964-980. ISSN 0377-2217
Full text not available from this repository.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.