用稀疏双流混元器提升多变量时序预测精度
SDMixer: Sparse Dual-Mixer for Time Series Forecasting
- 分频域与时域提取全局趋势与局部动态特征
- 通过稀疏机制过滤无效信息，提升变量依赖建模准确率
- 在多个真实数据集上表现领先，适合工业时序场景
多变量时间序列预测广泛应用于交通、能源和金融等领域。然而，数据常面临多尺度特性、弱相关性和噪声干扰问题，制约了现有模型的预测性能。本文提出一种双流稀疏混元预测框架，分别从频率域和时域中提取序列的全局趋势与局部动态特征，并引入稀疏机制过滤无效信息，从而增强跨变量依赖关系建模能力。实验结果表明，该方法在多个真实场景数据集上均取得领先性能，验证了其有效性与通用性。代码已开源：https://github.com/SDMixer/SDMixer。
原文摘要 · Abstract (English)
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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