New paper: Improving forecasting with sub-seasonal time series patterns

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

Published by

Yanfei Kang

Dr. Yanfei Kang is Associate Professor of Statistics at Beihang University in China. Prior to that, she was Senior R&D Engineer in Big Data Group of Baidu Inc. Yanfei obtained her Ph.D. degree at Monash University in 2014. She worked as a postdoctoral research fellow during 2014 and 2015 at Monash University. Her research interests include time series forecasting, time series visualization, text mining and statistical computing.

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