Authors: Xixi Li, Fotios Petropoulos, Yanfei Kang
Abstract: 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.
Links: Working paper
