对比两款时间序列模型对简单变量关系的建模能力
Investigating simple target-covariate relationships for Chronos-2 and TabPFN-TS
- 设计简化变量关系实验，测试模型捕捉依赖的能力
- TabPFN-TS在短时预测中表现优于Chronos-2
- 揭示强基准性能不等于良好变量建模能力
时间序列基础模型（TSFMs）近期取得领先性能，常在零样本设置下超越监督模型。如Chronos-2和TabPFN-TS等新架构致力于整合协变量信息。本文基于简单目标-协变量关系设计受控实验，评估其集成能力。结果表明，与Chronos-2相比，TabPFN-TS更能有效捕捉这些关系，尤其在短时预测中表现更优，说明Chronos-2的强基准表现并不自动转化为对简单协变量-目标依赖的最优建模。
原文摘要 · Abstract (English)
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.
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