{"ID":6029850,"CreatedAt":"2026-07-08T02:57:47.77373338Z","UpdatedAt":"2026-07-10T16:53:03.087978397Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.06504","arxiv_id":"2607.06504","title":"RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models","abstract":"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 time series archive that contains around 200 datasets and 142 billion time points across diverse domains. Furthermore, we pretrain four advanced TSFMs on univariate, synthetic multivariate, and real-world multivariate data and evaluate their zero-shot generalization capabilities on standard in-distribution and out-of-distribution benchmarks. Experimental results show that incorporating real-world multivariate data predominantly improves the generalization performance for both univariate and multivariate TSFMs. These results provide a deeper understanding of how real-world multivariate data contributes to the development of stronger TSFMs.","short_abstract":"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 dy...","url_abs":"https://arxiv.org/abs/2607.06504","url_pdf":"https://arxiv.org/pdf/2607.06504v1","authors":"[\"Qian Sun\",\"Yong-Ming Tian\",\"Jia-Wei Huang\",\"Cheng Feng\",\"Shao-Qun Zhang\"]","published":"2026-07-07T17:04:44Z","proceeding":"cs.AI","tasks":"[\"cs.AI\"]","methods":"[]","has_code":false}