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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - A New Taxonomy for Vector Exponential Smoothing and Its Application to Seasonal Time Series
AU - Svetunkov, Ivan
AU - Chen, Huijing
AU - Boylan, John E.
PY - 2023/2/1
Y1 - 2023/2/1
N2 - 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.
AB - 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.
KW - Forecasting
KW - Multivariate statistics
KW - Seasonal data
KW - Vector exponential smoothing
KW - Retailing
U2 - 10.1016/j.ejor.2022.04.040
DO - 10.1016/j.ejor.2022.04.040
M3 - Journal article
VL - 304
SP - 964
EP - 980
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 3
ER -