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arXiv · 2510.16060

Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated?

Coen Adler·Yuxin Chang·Felix Draxler·Samar Abdi·Padhraic Smyth
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Abstract

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.

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Coen Adler, Yuxin Chang, Felix Draxler, Samar Abdi, Padhraic Smyth. 2025-10-17. Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated?. https://arxiv.org/abs/2510.16060

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