Researchers have introduced RMISC, a large-scale real-world multivariate corpus designed to improve the performance of time series foundation models (TSFMs) by capturing complex temporal dynamics and cross-variable relationships. TSFMs have shown promise in achieving zero-shot generalization, but their reliance on synthetic data may limit their ability to model real-world phenomena. RMISC addresses this limitation by providing a diverse dataset that can be used to pretrain and fine-tune TSFMs, enabling them to better capture the nuances of real-world time series data. The development of RMISC has significant implications for the field of time series modeling, as it can help to improve the accuracy and robustness of TSFMs in a variety of applications1. This matters to practitioners because it can enhance the reliability of time series forecasting and anomaly detection, which are critical in many fields, including finance, healthcare, and cybersecurity, where accurate predictions and timely alerts can have significant consequences.