Mixed pooling of seasonality for time series forecasting: An application to pallet transport data

• Published in: Expert systems with applications 2022-09, Vol.201, p.117195, Article 117195
• Main Authors: Moon, Hyunji, Lee, Hyeonseop, Song, Bomi
• Format: Article
• Language: English
• Subjects: Decomposition; Forecasting; Hierarchical model; Mathematical models; Multiple seasonality; Pallet transport data; Pallets; Parameters; Predictions; Seasonal variations; Time series; Time series forecasting
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.117195

•Mixed hierarchical seasonality model is suggested.•The model addresses periodic scale jump and seasonal interaction problem.•The model showed improved accuracy and interpretability with pallet transport data. Multiple seasonal patterns, which often interact with each other, play a key role in time series forecasting, especially for business time series where seasonal effects are often dramatic. Previous approaches including Fourier decomposition, exponential smoothing, and seasonal autoregressive integrated moving average (SARIMA) models do not reflect the distinct characteristics of each period in seasonal patterns. We propose a mixed hierarchical seasonality (MHS) model. Intermediate parameters for each seasonal period are first estimated, and a mixture of intermediate parameters is taken. This results in a model that automatically learns the relative importance of each seasonality and addresses the interactions between them. The model is implemented with Stan, a probabilistic language, and was compared with three existing models on a real-world dataset of pallet transport from a logistic network. Our new model achieved considerable improvements in terms of out of sample prediction error and predictive density compared to complete pooling, Fourier decomposition, and SARIMA model.