Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysis

TL;DR

Time-MMD introduces a comprehensive multimodal time series dataset across nine domains and a forecasting library, enabling improved analysis by integrating textual and numerical data, leading to significant performance gains.

Contribution

This work provides the first multi-domain, multimodal time series dataset and a dedicated forecasting library, facilitating advanced multimodal TSA research and applications.

Findings

Over 15% MSE reduction in general multimodal TSF tasks

Up to 40% MSE improvement in textual-rich domains

Enhanced performance by extending unimodal to multimodal TSF

Abstract

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-cut 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 is available at https://github.com/AdityaLab/Time-MMD.