RMISC

RMISC: Large-scale real-world multivariate corpus for time series foundation model pretraining

Researchers introduce RMISC, an openly accessible corpus of ~200 datasets and 142 billion time points of real-world multivariate time series data, designed to address the gap between synthetic and real-world pretraining data for time series foundation models (TSFMs). Four advanced TSFMs are pretrained on univariate, synthetic multivariate, and real-world multivariate data and evaluated on zero-shot generalization benchmarks. Results show that real-world multivariate pretraining data consistently improves generalization for both univariate and multivariate TSFMs. The work provides both a reusable dataset resource and empirical evidence on the synthetic-vs-real data question for time series modeling.