通过多尺度频域掩码，提升多变量长期时间序列预测精度
MMFNet: Multi-Scale Frequency Masking Neural Network for Multivariate Time Series Forecasting
- 将时间序列分解为多尺度频段，用可学习掩码动态过滤无关成分
- 在多个基准数据集上，均方误差比现有模型降低最高6.0%
- 适合需要捕捉长短周期波动的电力、金融等长期预测场景
长期时间序列预测（LTSF）在电力消费规划、金融预测和疾病传播分析等众多现实应用中至关重要。LTSF需捕捉输入与输出间的长程依赖关系，但复杂的时序动态和高计算需求带来挑战。尽管线性模型通过频域分解降低复杂度，现有方法常假设平稳性，并滤除可能包含关键短期波动的高频成分。本文提出MMFNet，一种新型多变量长期预测模型，采用多尺度掩码频域分解方法，通过不同尺度的频段转换，捕捉精细、中等和粗粒度的时间模式，并利用可学习掩码自适应过滤无关成分。在多个基准数据集上的大量实验表明，MMFNet不仅克服了现有方法的局限性，且持续表现优异。具体而言，在多变量预测任务中，其均方误差（MSE）相比先进模型最高降低6.0%。
原文摘要 · Abstract (English)
Long-term Time Series Forecasting (LTSF) is critical for numerous real-world applications, such as electricity consumption planning, financial forecasting, and disease propagation analysis. LTSF requires capturing long-range dependencies between inputs and outputs, which poses significant challenges due to complex temporal dynamics and high computational demands. While linear models reduce model complexity by employing frequency domain decomposition, current approaches often assume stationarity and filter out high-frequency components that may contain crucial short-term fluctuations. In this paper, we introduce MMFNet, a novel model designed to enhance long-term multivariate forecasting by leveraging a multi-scale masked frequency decomposition approach. MMFNet captures fine, intermediate, and coarse-grained temporal patterns by converting time series into frequency segments at varying scales while employing a learnable mask to filter out irrelevant components adaptively. Extensive experimentation with benchmark datasets shows that MMFNet not only addresses the limitations of the existing methods but also consistently achieves good performance. Specifically, MMFNet achieves up to 6.0% reductions in the Mean Squared Error (MSE) compared to state-of-the-art models designed for multivariate forecasting tasks.
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