Multi-modal Time Series Analysis: A Tutorial and Survey

• URL: http://arxiv.org/abs/2503.13709v1
• Date: Mon, 17 Mar 2025 20:30:02 GMT
• Title: Multi-modal Time Series Analysis: A Tutorial and Survey
• Authors: Yushan Jiang, Kanghui Ning, Zijie Pan, Xuyang Shen, Jingchao Ni, Wenchao Yu, Anderson Schneider, Haifeng Chen, Yuriy Nevmyvaka, Dongjin Song,
• Abstract summary: Multi-modal time series analysis has emerged as a prominent research area in data mining.<n>However, effective analysis of multi-modal time series is hindered by data heterogeneity, modality gap, misalignment, and inherent noise.<n>Recent advancements in multi-modal time series methods have exploited the multi-modal context via cross-modal interactions.
• Score: 36.93906365779472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-modal time series analysis has recently emerged as a prominent research area in data mining, driven by the increasing availability of diverse data modalities, such as text, images, and structured tabular data from real-world sources. However, effective analysis of multi-modal time series is hindered by data heterogeneity, modality gap, misalignment, and inherent noise. Recent advancements in multi-modal time series methods have exploited the multi-modal context via cross-modal interactions based on deep learning methods, significantly enhancing various downstream tasks. In this tutorial and survey, we present a systematic and up-to-date overview of multi-modal time series datasets and methods. We first state the existing challenges of multi-modal time series analysis and our motivations, with a brief introduction of preliminaries. Then, we summarize the general pipeline and categorize existing methods through a unified cross-modal interaction framework encompassing fusion, alignment, and transference at different levels (\textit{i.e.}, input, intermediate, output), where key concepts and ideas are highlighted. We also discuss the real-world applications of multi-modal analysis for both standard and spatial time series, tailored to general and specific domains. Finally, we discuss future research directions to help practitioners explore and exploit multi-modal time series. The up-to-date resources are provided in the GitHub repository: https://github.com/UConn-DSIS/Multi-modal-Time-Series-Analysis

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