SDMixer: Sparse Dual-Mixer for Time Series Forecasting
This work addresses forecasting challenges in domains like transportation and finance, but it appears incremental as it builds on existing Mixer architectures with sparsity mechanisms.
The paper tackles the problem of multivariate time series forecasting by proposing a dual-stream sparse Mixer framework to address issues like multi-scale characteristics and noise, achieving leading performance on multiple real-world datasets.
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