Theta 모형의 개선된 계절성 처리 절차

초록

This study refines a seasonality procedure commonly applied in Theta-based forecasting models and assesses its impact on the forecasting performance of the DOTM(dynamic optimized theta model), one of the representative Theta models. Existing Theta-based models typically diagnose seasonality using lag-m autocorrelation and apply classical multiplicative decomposition when seasonality is present. We introduce the Kruskal–Wallis test and the Modified QS test for more reliable seasonality identification, and employ sophisticated seasonal-adjustment methods based on the exponential smoothing model and STL decomposition. Using quarterly and monthly time series from the M3 and M4 datasets, we conduct extensive forecasting experiments. The results show that the DOTM adopting the proposed seasonality procedure achieves better forecasting accuracy than the conventional approach, particularly in terms of symmetric mean absolute percentage and mean absolute percentage errors.

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