SDMixer: Sparse Dual-Mixer for Time Series Forecasting

TL;DR

SDMixer introduces a dual-stream sparse framework for multivariate time series forecasting, effectively capturing global and local features in frequency and time domains, leading to improved accuracy across diverse real-world datasets.

Contribution

The paper presents a novel dual-stream sparse Mixer model that enhances multivariate time series forecasting by integrating frequency and time domain features with a sparsity mechanism.

Findings

Achieves state-of-the-art performance on multiple datasets

Effectively models cross-variable dependencies

Robust to noise and multi-scale data

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