Multi-modal Time Series Analysis: A Tutorial and Survey

TL;DR

The paper tackles the challenge of extracting actionable insights from time series data that are augmented with diverse contexts from multiple modalities. It introduces a unified cross-modal interaction framework—encompassing fusion, alignment, and transference—applied across input, intermediate, and output stages, and provides a systematic taxonomy and up-to-date surveys of datasets and methods. Contributions include over 40 methods cataloged, domain-spanning applications (e.g., healthcare, finance, transportation, environment), and guidance on future directions such as reasoning augmentation, domain generalization, and robustness. The work offers a practical, structured reference for researchers and practitioners, with an accessible GitHub resource for reproducibility and exploration.

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

Figures (2)

• Figure 1: The framework of our tutorial and survey.
• Figure 2: Categorization of cross-modal interaction methods and representative examples.