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.

该研究解决了长期时间序列预测中的复杂依赖性和高计算需求的问题。通过引入一种多尺度遮蔽频率分解的新模型MMFNet，能够更有效地捕捉时间序列中的细粒度和粗粒度模式。实验结果表明，MMFNet在多变量预测任务中相比于现有方法可降低最高6%的均方误差，展现了显著的性能提升。

MMFNet：用于多变量时间序列预测的多尺度频率遮蔽神经网络