MMFNet：用于多元时间序列预测的多尺度频率掩码神经网络
MMFNet: Multi-Scale Frequency Masking Neural Network for Multivariate Time Series Forecasting
AI总结：
本文提出MMFNet，通过多尺度掩码频率分解捕捉不同粒度时间模式，以可学习掩码自适应过滤无关成分，在多元长期时间序列预测中相比现有最优模型将MSE最多降低6.0%。
摘要：
长期时间序列预测（LTSF）对众多现实应用至关重要，例如电力消耗规划、金融预测和疾病传播分析。LTSF需要捕捉输入与输出之间的长程依赖关系，由于复杂的时间动态和高计算需求，这带来了重大挑战。虽然线性模型通过采用频域分解来降低模型复杂度，但当前方法通常假设平稳性，并过滤掉可能包含关键短期波动的高频成分。在本文中，我们提出了MMFNet，这是一种新颖的模型，旨在通过利用多尺度掩码频率分解方法来增强长期多元预测。MMFNet通过将时间序列转换为不同尺度下的频率片段，同时采用可学习的掩码来自适应地过滤不相关成分，从而捕捉细粒度、中粒度和粗粒度的时间模式。在基准数据集上的大量实验表明，MMFNet不仅解决了现有方法的局限性，而且持续取得了良好的性能。具体而言，与为多元预测任务设计的最先进模型相比，MMFNet在均方误差（MSE）上实现了最高6.0%的降低。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
