RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization. Modern multivariate TSFMs are predominantly pretrained on multivariate synthetic data, which is easier to scale but may fail to capture the complex temporal dynamics and cross-variable relationships present in real-world time series. This raises a key question: Whether and to what extent the leading TSFMs trained with the real-world corpus perform better than those trained with synthetic data? To answer this, we establish the RMISC corpus, a considerably large-scale, high-quality, openly accessible, real-world, and multivariate ti...
| Authors | Qian Sun, Yong-Ming Tian, Jia-Wei Huang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 July 2026 |
| Last updated at source | 7 July 2026 |
Models and datasets referencing this paper
- nju-zhangsq/RMISCDataset
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