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RMISC: A Large-scale Real-world Multivariate Corpus... | AI Research

Key Takeaways

  • RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models Time Series Foundation Models (TSFMs) have become a powerful tool for f...
  • Recent years have witnessed the emergence of multivariate modeling using time series foundation models (TSFMs), which achieve advanced zero-shot generalization.
  • 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?
  • 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.
Paper AbstractExpand

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.

RMISC: A Large-scale Real-world Multivariate Corpus for Time Series Foundation Models
Time Series Foundation Models (TSFMs) have become a powerful tool for forecasting, but they are currently limited by how they are trained. Most modern models rely on synthetic data—data generated by computer algorithms—because it is easy to scale. However, synthetic data often fails to capture the complex, messy, and interconnected relationships found in real-world data. This paper introduces the RMISC corpus, a massive, high-quality collection of real-world multivariate time series data, to determine if training models on authentic data leads to better performance than training them on synthetic alternatives.

The RMISC Corpus

The researchers developed the Real-world Multivariate tIme Series Corpus (RMISC) to provide a more accurate foundation for training TSFMs. The archive contains approximately 200 datasets and 142 billion time points across diverse fields, including energy, finance, environment, industry, and traffic. Unlike synthetic data, which relies on simplified mathematical assumptions, RMISC captures the actual temporal dynamics and cross-variable dependencies—such as how wind speed and rainfall collectively influence temperature—that are essential for accurate real-world forecasting.

How the Corpus Was Built

Creating the RMISC was a rigorous five-stage engineering process. It began with sourcing data from open and legal real-world archives. The team then processed the data to handle noise, missing values, and outliers. They unified the data into a consistent, hierarchical structure to make it easy for researchers to use. A key part of the process involved creating a standardized metadata system, which ensures that every dataset is traceable and includes information about its origin and licensing. Finally, the team refined the data to ensure it was reliable and balanced across different domains, providing both a full version and a smaller, domain-balanced version for easier experimentation.

Impact on Model Performance

To test the value of this new corpus, the researchers pretrained four advanced TSFMs—Chronos-2, GTT, Moirai-2.0, and TimesFM-2.5—using three different approaches: univariate data, synthetic multivariate data, and the real-world RMISC data. The experiments revealed that models trained with real-world multivariate data consistently outperformed those trained on synthetic or univariate data. Specifically, incorporating real-world data improved the models' ability to generalize to new, unseen scenarios (out-of-distribution performance). The study concludes that the best results come from a balanced recipe: combining real-world univariate data, synthetic multivariate data, and the new real-world multivariate RMISC data.

Key Takeaways

The findings suggest that while synthetic data is useful for scaling, it is not a complete substitute for the complexity of the real world. By providing an openly accessible, large-scale, and high-quality archive, the authors aim to help developers build more robust TSFMs that can handle the intricate relationships present in actual time series data. The RMISC corpus is now available to the research community to support further development and benchmarking of multivariate foundation models.

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