  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.
