A new taxonomy for vector exponential smoothing and its application to seasonal time series

Ivan Svetunkov*, Huijing Chen, John E. Boylan

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

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    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.
    Original languageEnglish
    JournalEuropean Journal of Operational Research
    Early online date4 May 2022
    DOIs
    Publication statusEarly online - 4 May 2022

    Keywords

    • Forecasting
    • Multivariate statistics
    • Seasonal data
    • Vector exponential smoothing
    • Retailing

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