Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series

TL;DR

Time-IMM introduces a new dataset and benchmark for irregular, multimodal, multivariate time series, addressing real-world challenges and enabling more realistic forecasting evaluations.

Contribution

It provides the first dataset capturing cause-driven irregularities in multimodal time series and a benchmark library with specialized fusion modules for improved forecasting.

Findings

Explicit modeling of multimodality improves forecasting accuracy.

The dataset captures nine types of irregularity in real-world data.

Benchmark results show substantial performance gains with proposed methods.

Abstract

Time series data in real-world applications such as healthcare, climate modeling, and finance are often irregular, multimodal, and messy, with varying sampling rates, asynchronous modalities, and pervasive missingness. However, existing benchmarks typically assume clean, regularly sampled, unimodal data, creating a significant gap between research and real-world deployment. We introduce Time-IMM, a dataset specifically designed to capture cause-driven irregularity in multimodal multivariate time series. Time-IMM represents nine distinct types of time series irregularity, categorized into trigger-based, constraint-based, and artifact-based mechanisms. Complementing the dataset, we introduce IMM-TSF, a benchmark library for forecasting on irregular multimodal time series, enabling asynchronous integration and realistic evaluation. IMM-TSF includes specialized fusion modules, including a timestamp-to-text fusion module and a multimodality fusion module, which support both recency-aware averaging and attention-based integration strategies. Empirical results demonstrate that explicitly modeling multimodality on irregular time series data leads to substantial gains in forecasting performance. Time-IMM and IMM-TSF provide a foundation for advancing time series analysis under real-world conditions. The dataset is publicly available at https://github.com/blacksnail789521/Time-IMM, and the benchmark library can be accessed at https://github.com/blacksnail789521/IMM-TSF. Project page: https://blacksnail789521.github.io/time-imm-project-page/