A novel dual-channel model with adaptive multi-scale attention for time series forecasting

Abstract

Time series forecasting plays a crucial role in various domains, including finance, traffic management, energy, and healthcare. However, as application scenarios continue to expand, the complexity of time series data has significantly increased, posing substantial challenges in capturing trend fluctuations of multivariate features and the dynamic relationships among them. To address these issues, this paper proposes a novel architecture–DASformer (Dual-Channel model with Adaptive multi-Scale attention) - which enhances time series analysis by leveraging a dual-channel multivariate extractor and an adaptive multi-scale attention mechanism. Specifically, the dual-channel multivariate extractor comprises two independent yet interactive streams, focusing on capturing information at different levels of the time series, thereby effectively decoupling complex dynamic relationships. Moreover, to alleviate the problem of feature forgetting and loss in the long-term trend stream, the model incorporates an adaptive multi-scale attention module. This module adopts multi-scale processing and a dynamic weighting mechanism to learn dependencies across different scales and effectively capture their dynamic variations. Experimental results show that DASformer consistently achieves state-of-the-art performance on nine widely used benchmark datasets, delivering superior prediction accuracy, particularly in long-term forecasting tasks. The source code is available at: https://github.com/LDU-TSA/DASformer.