MoTime: A Dataset Suite for Multimodal Time Series Forecasting
This work addresses the problem of limited multimodal benchmarks for researchers in time series forecasting, though it is incremental as it focuses on dataset creation rather than novel methods.
The authors tackled the lack of multimodal datasets for time series forecasting by introducing MoTime, a suite that pairs temporal signals with external modalities like text and images, showing that these modalities can improve forecasting performance, with benefits varying by data characteristics.
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.