A Multiscale Transformer Model for Long Time Series Forecasting Based on Discrete Wavelet Transform and Residual Learning Modules

ABSTRACT Transformer‐based models have witnessed remarkable advancements in the domain of time series forecasting. However, significant challenges persist in effectively handling large volumes of historical data and comprehensively capturing multiscale characteristics inherent in time series. This paper proposes a novel time series forecasting model that integrates the Discrete Wavelet Transform (DWT) and residual learning modules. This integration is aimed at enhancing the model's proficiency in capturing the intricate nonlinear and multiscale features of time series data. The proposed model leverages DWT to decompose the time series into multiple scales, enabling it to effectively capture both local and global features across diverse temporal resolutions. The residual learning modules are meticulously designed to improve the training stability of the model and augment its feature extraction capabilities. Additionally, local and global attention mechanisms are employed to comprehensively capture short‐ and long‐term dependencies within time series data. Comprehensive experiments conducted on seven real‐world datasets demonstrate that the proposed approach outperforms state‐of‐the‐art deep learning models in long‐term time series forecasting tasks. It achieves higher accuracy and better generalization performance. Ablation studies are also carried out, which further validate the individual contributions of each module to the overall performance of the proposed model, providing strong evidence for the effectiveness of the model's design.