探索多模态如何提升时间序列分析，系统梳理最新研究方向
How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlook
- 将其他模态的预训练模型用于高效时间序列分析
- 通过跨模态交互增强时间序列建模能力
- 适合关注多模态融合与时间序列结合的研究者
时间序列分析（TSA）是数据挖掘领域的重要课题，具有广泛实际意义。相比语言、视觉等“丰富”模态近年来的迅猛发展，时间序列模态仍相对孤立。近期涌现出大量聚焦多模态时间序列分析（MM4TSA）的研究，核心问题为：时间序列分析如何从多模态中获益？本综述首次全面梳理该新兴领域，系统讨论三大收益：（1）复用其他模态的基础模型以实现高效TSA；（2）通过多模态扩展提升TSA性能；（3）利用跨模态交互实现高级分析。按引入模态类型（文本、图像、音频、表格等）分类整理工作，并指出未来方向：预训练模态选择、异构模态组合、未见任务泛化，对应前述三类收益。附最新开源资源库（GitHub）。
原文摘要 · Abstract (English)
Time series analysis (TSA) is a longstanding research topic in the data mining community and has wide real-world significance. Compared to "richer" modalities such as language and vision, which have recently experienced explosive development and are densely connected, the time-series modality remains relatively underexplored and isolated. We notice that many recent TSA works have formed a new research field, i.e., Multiple Modalities for TSA (MM4TSA). In general, these MM4TSA works follow a common motivation: how TSA can benefit from multiple modalities. This survey is the first to offer a comprehensive review and a detailed outlook for this emerging field. Specifically, we systematically discuss three benefits: (1) reusing foundation models of other modalities for efficient TSA, (2) multimodal extension for enhanced TSA, and (3) cross-modality interaction for advanced TSA. We further group the works by the introduced modality type, including text, images, audio, tables, and others, within each perspective. Finally, we identify the gaps with future opportunities, including the reused modalities selections, heterogeneous modality combinations, and unseen tasks generalizations, corresponding to the three benefits. We release an up-to-date GitHub repository that includes key papers and resources.
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