  Time series forecasting plays an increasingly important role in modern
business decisions. In today's data-rich environment, people often aim to
choose the optimal forecasting model for their data. However, identifying the
optimal model often requires professional knowledge and experience, making
accurate forecasting a challenging task. To mitigate the importance of model
selection, we propose a simple and reliable algorithm and successfully improve
the forecasting performance. Specifically, we construct multiple time series
with different sub-seasons from the original time series. These derived series
highlight different sub-seasonal patterns of the original series, making it
possible for the forecasting methods to capture diverse patterns and components
of the data. Subsequently, we make forecasts for these multiple series
separately with classical statistical models (ETS or ARIMA). Finally, the
forecasts of these multiple series are combined with equal weights. We evaluate
our approach on the widely-used forecasting competition datasets (M1, M3, and
M4), in terms of both point forecasts and prediction intervals. We observe
improvements in performance compared with the benchmarks. Our approach is
particularly suitable and robust for the datasets with higher frequencies. To
demonstrate the practical value of our proposition, we showcase the performance
improvements from our approach on hourly load data.
