系统梳理时空图神经网络在时序预测与分类中的方法与应用
A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification
- 系统整理366篇论文，归纳主流建模思路与技术路径
- 汇总公开数据集、基准模型与性能结果，便于复现与对比
- 揭示可比性、可解释性等核心挑战，适合研究者快速入门
近年来，时空图神经网络（GNN）因其能同时捕捉变量间依赖关系与时间点上的动态变化，在时序分析领域受到广泛关注。本文通过系统文献回顾，全面梳理了用于时序分类与预测的各类时空GNN建模方法及其应用领域。通过数据库检索，共筛选出366篇论文进行深入分析，旨在为读者提供当前最前沿模型、相关源码链接、可用数据集、基准模型及实验结果的综合参考。所有信息均有助于研究人员开展后续工作。据我们所知，这是首个且覆盖范围最广的系统综述，对不同领域的当前时空GNN模型进行了详细对比。文章最后讨论了该领域面临的挑战，包括模型可比性、可复现性、可解释性、信息容量不足与扩展性问题。本文还配套提供了一个GitHub仓库（https://github.com/FlaGer99/SLR-Spatio-Temporal-GNN.git），包含交互式工具以进一步探索研究成果。
原文摘要 · Abstract (English)
In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture, at once, dependencies among variables and across time points. The objective of this systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and 366 papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive review of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in their studies. To the best of our knowledge, this is the first and broadest systematic literature review presenting a detailed comparison of results from current spatio-temporal GNN models applied to different domains. In its final part, this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability. This paper is complemented by a GitHub repository at https://github.com/FlaGer99/SLR-Spatio-Temporal-GNN.git providing additional interactive tools to further explore the presented findings.
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