Enhancing Multivariate Time Series Forecasting with Multi-scale Moving Transformation

Wenjie Ou, Hongmin Du, Wenqiang Zhao, Dongyue Guo, Yi Lin

Abstract: Recently the performance of long-term time series forecasting has been greatly improved by deep models. In this paper, we propose a general framework called Multi-scale Moving Transformation (MMT) that can be applied to the state-of-the-art models of time series forecasting tasks. Via comparative experiments, we demonstrate the effectiveness of the MMT framework against the models with the moving average. By incorporating the MMT framework with state-of-the-art backbone models based on different methods, our experimental results on various public datasets demonstrate that the improvements outperform their corresponding baseline counterparts.