RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models

arXiv:2607.06504v1 Announce Type: new 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

Source: arXiv cs.AI — read the full report at the original publisher.

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