Forecasting with Multiple Seasonality

Tianyang Xie, Jie Ding

Abstract: Several modern applications involve forecasting time series data that exhibit both short-time dynamics and long-time seasonality. In particular, modeling time series with multiple seasonality is a challenging task with relatively few discussions. In this paper, we propose a two-stage method for predicting time series with multi-seasonality, which does not require predetermined seasonality periods. In the first stage, we generalize the classical seasonal autoregressive moving average model to multi-seasonality scenarios. In the second stage, we utilize an appropriate criterion for lag order selection. Simulation and empirical studies show the excellent predictive performance of our method, especially when compared with a recently popular `Facebook Prophet' model for time series.