The recent development of foundation models for time series data has generated considerable interest in using such models across a variety of applications. Although foundation models achieve state-of-the-art predictive performance, their calibration properties remain relatively underexplored, despite the fact that calibration can be critical for many practical applications. In this paper, we investigate the calibration-related properties of five recent time series foundation models and two competitive baselines. We perform a series of systematic evaluations assessing model calibration (i.e., over- or under-confidence), effects of varying prediction heads, and calibration under long-term autoregressive forecasting. We find that time series foundation models are consistently better calibrated than baseline models and tend not to be either systematically over- or under-confident, in contrast to the overconfidence often seen in other deep learning models.
@article{arxiv.2510.16060,
title = {Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated?},
author = {Coen Adler and Yuxin Chang and Felix Draxler and Samar Abdi and Padhraic Smyth},
journal= {arXiv preprint arXiv:2510.16060},
year = {2026}
}