Two-Stage Framework for Seasonal Time Series Forecasting
This addresses forecasting accuracy for seasonal time series, which is an incremental improvement over existing methods.
The authors tackled seasonal time series forecasting by proposing a two-stage framework that first learns long-range seasonal dependencies and then uses this to enhance predictions, achieving state-of-the-art performance on the M4 Competition Hourly datasets.
Seasonal time series Forecasting remains a challenging problem due to the long-term dependency from seasonality. In this paper, we propose a two-stage framework to forecast univariate seasonal time series. The first stage explicitly learns the long-range time series structure in a time window beyond the forecast horizon. By incorporating the learned long-range structure, the second stage can enhance the prediction accuracy in the forecast horizon. In both stages, we integrate the auto-regressive model with neural networks to capture both linear and non-linear characteristics in time series. Our framework achieves state-of-the-art performance on M4 Competition Hourly datasets. In particular, we show that incorporating the intermediate results generated in the first stage to existing forecast models can effectively enhance their prediction performance.