Calibration Properties of Time Series Foundation Models

The recent development of foundation models for time-series data has generated considerable interest in using such models across a variety of applications. Although they achieve state-of-the-art predictive performance, the ability to produce well-calibrated probabilistic distributions is critical for practical applications and is relatively underexplored. In this paper we investigate the calibration-related properties of four recent time-series foundation models and two competitive baselines. We perform systematic evaluations and identify significant variation in calibration performances across models.