SDMixer: Sparse Dual-Mixer for Time Series Forecasting
arXiv:2602.23581v1 Announce Type: new 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
arXiv:2602.23581v1 Announce Type: new 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
Executive Summary
The article introduces SDMixer, a novel dual-stream sparse Mixer framework for multivariate time series forecasting. SDMixer leverages a sparsity mechanism to extract global trends and local dynamic features from sequences in both frequency and time domains. This approach enhances the accuracy of cross-variable dependency modeling and yields leading performance on multiple real-world scenario datasets. Although the framework demonstrates effectiveness and generality, its limitations and potential applications warrant further exploration. The availability of the code on GitHub facilitates reproducibility and potential future improvements. This research has significant implications for fields relying on time series forecasting, including transportation, energy, and finance.
Key Points
- ▸ SDMixer employs a dual-stream sparse Mixer framework to extract global trends and local dynamic features.
- ▸ The sparsity mechanism enhances the accuracy of cross-variable dependency modeling.
- ▸ SDMixer achieves leading performance on multiple real-world scenario datasets.
Merits
Strength in Handling Multi-Scale Characteristics
SDMixer effectively extracts global trends and local dynamic features from sequences in both frequency and time domains, addressing the issue of multi-scale characteristics.
Improved Accuracy in Cross-Variable Dependency Modeling
The sparsity mechanism enhances the accuracy of cross-variable dependency modeling, leading to improved forecasting performance.
Demerits
Limited Generalizability to Noisy Data
The effectiveness of SDMixer may be compromised in scenarios with high levels of noise interference, limiting its generalizability.
Expert Commentary
SDMixer is a notable contribution to the field of time series forecasting, offering a novel framework that effectively addresses issues of multi-scale characteristics and noisy data. While its limitations, particularly in handling noisy data, warrant further exploration, the availability of the code on GitHub facilitates reproducibility and potential future improvements. As SDMixer has significant practical and policy implications, it is essential to continue refining and extending this framework to ensure its widespread adoption and impact.
Recommendations
- ✓ Future research should focus on adapting SDMixer to handle high levels of noise interference and explore its application in more complex time series forecasting scenarios.
- ✓ The development of SDMixer highlights the need for more robust and accurate time series forecasting frameworks, emphasizing the importance of continued research in this area.