Datasets:
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This dataset card describes the `main` branch of RMISC. RMISC is a large-scale, real-world multivariate time-series corpus for pretraining and benchmarking time-series foundation models (TSFMs). The complete corpus contains around 200 sub-datasets, 2 million original time-series files, 16 billion timesteps, and 142 billion time points across energy, finance, environment, industry, traffic, and other domains.
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- **Project GitHub:** [https://github.com/zhangsq-nju/RMISC](https://github.com/zhangsq-nju/RMISC)
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- **Paper:** [
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## 📦 Dataset Packaging & Structure
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If you use RMISC in your research, please cite:
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```bibtex
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@article{
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title = {RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models},
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author = {Sun, Qian and Tian, Yong-Ming and Huang, Jia-Wei and Feng, Cheng and Zhang, Shao-Qun},
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journal = {
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year = {2026}
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}
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```
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This dataset card describes the `main` branch of RMISC. RMISC is a large-scale, real-world multivariate time-series corpus for pretraining and benchmarking time-series foundation models (TSFMs). The complete corpus contains around 200 sub-datasets, 2 million original time-series files, 16 billion timesteps, and 142 billion time points across energy, finance, environment, industry, traffic, and other domains.
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- **Project GitHub:** [https://github.com/zhangsq-nju/RMISC](https://github.com/zhangsq-nju/RMISC)
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- **Paper:** [https://arxiv.org/abs/2607.06504](https://arxiv.org/abs/2607.06504)
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## 📦 Dataset Packaging & Structure
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If you use RMISC in your research, please cite:
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```bibtex
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@article{sun2026rmisclargescalerealworldmultivariate,
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title = {RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models},
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author = {Sun, Qian and Tian, Yong-Ming and Huang, Jia-Wei and Feng, Cheng and Zhang, Shao-Qun},
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journal = {arXiv preprint arXiv:2607.06504},
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year = {2026}
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}
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```
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