MSGNet：学习多尺度序列间相关性以进行多元时间序列预测
MSGNet: Learning Multi-Scale Inter-Series Correlations for Multivariate Time Series Forecasting
AI总结：
本文提出深度学习模型MSGNet，通过频域分析提取周期模式并分解时间尺度，结合自注意力机制和自适应mixhop图卷积层捕获多尺度序列间相关性，在真实数据集上展现出高效预测与强泛化能力。
摘要：
多元时间序列预测在各个学科中一直是一个持续的挑战。时间序列数据通常表现出多样的序列内和序列间相关性，导致了复杂且交织的依赖关系，这已成为众多研究的焦点。然而，在理解多个时间序列中不同时间尺度下的序列间相关性变化方面仍存在显著的研究空白，这一领域在文献中受到的关注有限。为了填补这一空白，本文引入了MSGNet，这是一种先进的深度学习模型，旨在利用频域分析和自适应图卷积来捕获跨多个时间尺度的变化的序列间相关性。通过利用频域分析，MSGNet有效地提取显著的周期性模式，并将时间序列分解为不同的时间尺度。该模型结合了自注意力机制来捕获序列内依赖关系，同时引入了自适应mixhop图卷积层，以在每个时间尺度内自主学习多样的序列间相关性。在几个真实世界数据集上进行了广泛的实验，以展示MSGNet的有效性。此外，MSGNet具备自动学习可解释的多尺度序列间相关性的能力，即使在应用于分布外样本时也展现出强大的泛化能力。
英文摘要：
Multivariate time series forecasting poses an ongoing challenge across various disciplines. Time series data often exhibit diverse intra-series and inter-series correlations, contributing to intricate and interwoven dependencies that have been the focus of numerous studies. Nevertheless, a significant research gap remains in comprehending the varying inter-series correlations across different time scales among multiple time series, an area that has received limited attention in the literature. To bridge this gap, this paper introduces MSGNet, an advanced deep learning model designed to capture the varying inter-series correlations across multiple time scales using frequency domain analysis and adaptive graph convolution. By leveraging frequency domain analysis, MSGNet effectively extracts salient periodic patterns and decomposes the time series into distinct time scales. The model incorporates a self-attention mechanism to capture intra-series dependencies, while introducing an adaptive mixhop graph convolution layer to autonomously learn diverse inter-series correlations within each time scale. Extensive experiments are conducted on several real-world datasets to showcase the effectiveness of MSGNet. Furthermore, MSGNet possesses the ability to automatically learn explainable multi-scale inter-series correlations, exhibiting strong generalization capabilities even when applied to out-of-distribution samples.
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。
