  Time series data are ubiquitous across a wide range of real-world domains.
While real-world time series analysis (TSA) requires human experts to integrate
numerical series data with multimodal domain-specific knowledge, most existing
TSA models rely solely on numerical data, overlooking the significance of
information beyond numerical series. This oversight is due to the untapped
potential of textual series data and the absence of a comprehensive,
high-quality multimodal dataset. To overcome this obstacle, we introduce
Time-MMD, the first multi-domain, multimodal time series dataset covering 9
primary data domains. Time-MMD ensures fine-grained modality alignment,
eliminates data contamination, and provides high usability. Additionally, we
develop MM-TSFlib, the first multimodal time-series forecasting (TSF) library,
seamlessly pipelining multimodal TSF evaluations based on Time-MMD for in-depth
analyses. Extensive experiments conducted on Time-MMD through MM-TSFlib
demonstrate significant performance enhancements by extending unimodal TSF to
multimodality, evidenced by over 15% mean squared error reduction in general,
and up to 40% in domains with rich textual data. More importantly, our datasets
and library revolutionize broader applications, impacts, research topics to
advance TSA. The dataset and library are available at
https://github.com/AdityaLab/Time-MMD and
https://github.com/AdityaLab/MM-TSFlib.
