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 cor
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- PossiblePossibly related (embedding) · 50%From virtual experiments to biomedical insight with synthetic data →
- PossiblePossibly related (embedding) · 49%sktime/sktime →
- LinkedLinked via arxiv author · 85%Qian Sun →
“RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models”
- LinkedLinked via arxiv author · 85%Yong-Ming Tian →
“RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models”
- LinkedLinked via arxiv author · 85%Jia-Wei Huang →
“RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models”
- LinkedLinked via arxiv author · 85%Cheng Feng →
“RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models”
- LinkedLinked via arxiv author · 85%Shao-Qun Zhang →
“RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models”
- FuzzyOverlapping authors or contributors · 62%google-research/google-research →
“Shared author/contributor keys: sun”