Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
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Abstract (arXiv)
Time Series Foundation Models (TSFMs) have recently achieved state-of-the-art performance, often outperforming supervised models in zero-shot settings. Recent TSFM architectures, such as Chronos-2 and TabPFN-TS, aim to integrate covariates. In this paper, we design controlled experiments based on simple target-covariate relationships to assess this integration capability. Our results show that TabPFN-TS captures these relationships more effectively than Chronos-2, especially for short horizons, suggesting that the strong benchmark performance of Chronos-2 does not automatically translate into optimal modeling of simple covariate-target dependencies.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 13 references that are in the map. First posted on arXiv 12 May 2026.
Builds on
9 of 13 references.
References · 9
- 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 869
- 2024Chronos: Learning the Language of Time Seriescited by 712
- 2023A decoder-only foundation model for time-series forecastingcited by 650
- 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
- 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
- 2024GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluationcited by 188
- 2025Chronos-2: From Univariate to Universal Forecastingcited by 158
- 2025Moirai 2.0: When Less Is More for Time Series Forecastingcited by 59
- 2025fev-bench: A Realistic Benchmark for Time Series Forecastingcited by 52
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Citing papers per year: 2026: 1.
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