[2602.23581] SDMixer: Sparse Dual-Mixer for Time Series Forecasting

Computer Science > Machine Learning arXiv:2602.23581 (cs) [Submitted on 27 Feb 2026] Title:SDMixer: Sparse Dual-Mixer for Time Series Forecasting Authors:Xiang Ao View a PDF of the paper titled SDMixer: Sparse Dual-Mixer for Time Series Forecasting, by Xiang Ao View PDF HTML (experimental) Abstract:Multivariate time series forecasting is widely applied in fields such as transportation, energy, and finance. However, the data commonly suffers from issues of multi-scale characteristics, weak correlations, and noise interference, which limit the predictive performance of existing models. This paper proposes a dual-stream sparse Mixer prediction framework that extracts global trends and local dynamic features from sequences in both the frequency and time domains, respectively. It employs a sparsity mechanism to filter out invalid information, thereby enhancing the accuracy of cross-variable dependency modeling. Experimental results demonstrate that this method achieves leading performance on multiple real-world scenario datasets, validating its effectiveness and generality. The code is available at this https URL Comments: Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI) Cite as: arXiv:2602.23581 [cs.LG]   (or arXiv:2602.23581v1 [cs.LG] for this version)   https://doi.org/10.48550/arXiv.2602.23581 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xiang Ao [view email] [v1] Fri, 27 Feb 2026 01:13:56 UTC (543 KB) ...