arXiv 2506.104122025-10-15cs.LGcs.AIcs.CL

Time-IMM：用于不规则多模态多元时间序列的数据集与基准

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

University of California, Los Angeles（加州大学洛杉矶分校）；National Yang Ming Chiao Tung University（国立阳明交通大学）

Ching Chang, Jeehyun Hwang, Yidan Shi, Haixin Wang, Wen-Chih Peng, Tien-Fu Chen, Wei Wang

备注：: This paper has been accepted by the NeurIPS 2025 Datasets and Benchmarks Track

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

针对现实时间序列的不规则、多模态特性，提出Time-IMM数据集和IMM-TSF基准库，通过显式建模多模态性显著提升预测性能。

摘要：

现实世界应用（如医疗保健、气候建模和金融）中的时间序列数据通常是不规则的、多模态的和杂乱的，具有不同的采样率、异步模态和普遍缺失。然而，现有基准通常假设数据是干净、规则采样和单模态的，这造成了研究与实际部署之间的显著差距。我们引入了Time-IMM，这是一个专门设计用于捕获多模态多元时间序列中因果驱动的不规则性的数据集。Time-IMM代表了九种不同类型的时间序列不规则性，分为基于触发、基于约束和基于伪影的机制。作为数据集的补充，我们引入了IMM-TSF，一个用于不规则多模态时间序列预测的基准库，支持异步集成和现实评估。IMM-TSF包括专门的融合模块，包括时间戳到文本融合模块和多模态融合模块，支持近期感知平均和基于注意力的集成策略。实证结果表明，在不规则时间序列数据上显式建模多模态性可以显著提高预测性能。Time-IMM和IMM-TSF为在现实条件下推进时间序列分析提供了基础。数据集可在https://github.com/blacksnail789521/Time-IMM公开获取，基准库可在https://github.com/blacksnail789521/IMM-TSF访问。项目页面：https://blacksnail789521.github.io/time-imm-project-page/

英文摘要：

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/

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。