SDMixer: Sparse Dual-Mixer for Time Series Forecasting

TL;DR

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 and employs a sparsity mechanism to filter out invalid information, thereby enhancing the accuracy of cross-variable dependency modeling.

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

Figures (2)

• Figure 1: Framework of SDMixer.
• Figure 2: Performance of ablation models on different datasets (errors for prediction length 96).