arXiv 2307.037592024-08-09cs.LGcs.AI

图神经网络用于时间序列综述：预测、分类、插补与异常检测

A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

Griffith University（格里菲斯大学）；Monash University（莫纳什大学）；Squirrel AI（松鼠AI）；IDSIA（瑞士IDSIA人工智能实验室）；Università della Svizzera italiana（意大利语瑞士大学）；Politecnico di Milano（米兰理工大学）；The Chinese University of Hong Kong（香港中文大学）

Ming Jin, Huan Yee Koh, Qingsong Wen, Daniele Zambon, Cesare Alippi, Geoffrey I. Webb, Irwin King, Shirui Pan

备注：: 37 pages, 6 figures, 7 tables; Project page: https://github.com/KimMeen/Awesome-GNN4TS
发表信息：: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024

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AI总结：

本文综述 GNN4TS 在预测、分类、插补和异常检测四类任务中的方法、分类体系、代表工作、应用及未来方向。

摘要：

时间序列是用于记录动态系统测量值的主要数据类型，由物理传感器和在线过程（虚拟传感器）大量生成。因此，时间序列分析对于释放可用数据中隐含的丰富信息至关重要。随着图神经网络（GNN）的最新进展，基于 GNN 的时间序列分析方法大量涌现。这些方法能够显式建模时间之间与变量之间的关系，而传统方法及其他基于深度神经网络的方法难以做到这一点。在本综述中，我们全面回顾了用于时间序列分析的图神经网络（GNN4TS），涵盖四个基本维度：预测、分类、异常检测和插补。我们的目标是指导设计者和从业者理解、构建应用并推进 GNN4TS 研究。首先，我们提供了全面的、面向任务的 GNN4TS 分类体系。随后，我们介绍并讨论了具有代表性的研究工作，并引入 GNN4TS 的主流应用。最后，本综述对潜在的未来研究方向进行了全面讨论。本综述首次汇集了基于 GNN 的时间序列研究的大量知识，重点阐述了图神经网络用于时间序列分析的基础、实际应用和机遇。

英文摘要：

Time series are the primary data type used to record dynamic system measurements and generated in great volume by both physical sensors and online processes (virtual sensors). Time series analytics is therefore crucial to unlocking the wealth of information implicit in available data. With the recent advancements in graph neural networks (GNNs), there has been a surge in GNN-based approaches for time series analysis. These approaches can explicitly model inter-temporal and inter-variable relationships, which traditional and other deep neural network-based methods struggle to do. In this survey, we provide a comprehensive review of graph neural networks for time series analysis (GNN4TS), encompassing four fundamental dimensions: forecasting, classification, anomaly detection, and imputation. Our aim is to guide designers and practitioners to understand, build applications, and advance research of GNN4TS. At first, we provide a comprehensive task-oriented taxonomy of GNN4TS. Then, we present and discuss representative research works and introduce mainstream applications of GNN4TS. A comprehensive discussion of potential future research directions completes the survey. This survey, for the first time, brings together a vast array of knowledge on GNN-based time series research, highlighting foundations, practical applications, and opportunities of graph neural networks for time series analysis.

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。