| title | Forecast Collapse in Time-Series Foundation Models |
|---|---|
| authors | Shu Wan, Miles Ma, Hank Zhu, Guangqi Liu, Stephen Wang, Qingsong Wen, Huan Liu |
| date | 2026-08-14 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.14106 |
| affiliation | Abel AI |
| upvotes | 10 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation. We call this forecast collapse. Surprisingly, the phenomenon largely disappears when forecasting trading volume under the same setting. We investigate forecast collapse across time-series foundation models (TSFMs), twelve deep-learning forecasting models, and 97 public benchmark configurations, and find that it is closely tied to target predictability. We identify two distinct reasons behind it: low predictability limits the amplitude of calibrated point forecasts, while per-series objectives leave cross-series structure unidentified. These findings reveal a calibration-ranking tradeoff: optimizing squared error leads to flat predictions, whereas directly optimizing cross-sectional correlation improves ranking but can inflate forecast amplitude by more than an order of magnitude. To address this tradeoff, we introduce CalibRank, a simple objective that balances calibration and ranking. On Finance1K, CalibRank nearly triples cross-sectional correlation while keeping amplitude close to the target, and improves correlation on all tested models. Our results reveal a blind spot in conventional time-series evaluation: per-series metrics can hide failures in cross-series structure needed by downstream decisions. |
cite
@misc{wan2026forecast,
title={Forecast Collapse in Time-Series Foundation Models},
author={Shu Wan and Miles Ma and Hank Zhu and Guangqi Liu and Stephen Wang and Qingsong Wen and Huan Liu},
year={2026},
eprint={2608.14106},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.14106}
}