SDMixer: Sparse Dual-Mixer for Time Series Forecasting

• URL: http://arxiv.org/abs/2602.23581v1
• Date: Fri, 27 Feb 2026 01:13:56 GMT
• Title: SDMixer: Sparse Dual-Mixer for Time Series Forecasting
• Authors: Xiang Ao,
• Abstract summary: This paper proposes a dual-stream sparse prediction framework that extracts global trends and local dynamic features from sequences in both the frequency and time domains.<n>It employs a sparsity mechanism to filter out invalid information, thereby enhancing the accuracy of cross-variable dependency modeling.
• Score: 8.124083509364981
• License: http://creativecommons.org/licenses/by/4.0/
• 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 https://github.com/SDMixer/SDMixer

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