构建多模态时间序列数据集，支持真实场景下的预测评估
MoTime: A Dataset Suite for Multimodal Time Series Forecasting
- 设计涵盖文本/图像/元数据的多模态时间序列数据集
- 外源模态在有无历史数据时均提升预测效果
- 适合做多模态时序建模与真实场景基准测试的研究者
尽管现实世界中的多模态数据日益丰富，现有研究仍以单模态时间序列为主。本文提出MoTime，一套包含时序信号与外部模态（如文本、元数据、图像）的多模态时间序列预测数据集。覆盖多个领域，支持两种场景下的结构化评估：1）常规预测任务（可提供变长历史）；2）冷启动预测（无历史数据）。实验表明，外部模态在两种场景下均能提升预测性能，尤其在部分数据集的短序列中表现显著，但效果随数据特征而异。通过公开数据集与结果，旨在推动未来多模态时间序列预测研究的更全面、更真实的基准建设。
原文摘要 · Abstract (English)
While multimodal data sources are increasingly available from real-world forecasting, most existing research remains on unimodal time series. In this work, we present MoTime, a suite of multimodal time series forecasting datasets that pair temporal signals with external modalities such as text, metadata, and images. Covering diverse domains, MoTime supports structured evaluation of modality utility under two scenarios: 1) the common forecasting task, where varying-length history is available, and 2) cold-start forecasting, where no historical data is available. Experiments show that external modalities can improve forecasting performance in both scenarios, with particularly strong benefits for short series in some datasets, though the impact varies depending on data characteristics. By making datasets and findings publicly available, we aim to support more comprehensive and realistic benchmarks in future multimodal time series forecasting research.
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