时间序列基础模型中的预测崩溃

摘要

当我们预测1000只美国股票的每小时收益率时，观察到一种意外现象：预测结果近乎平坦，且以横截面相关性衡量的股票排序能力较差。我们将此称为预测崩溃。令人惊讶的是，在相同设置下预测交易量时，该现象基本消失。我们跨时间序列基础模型（TSFMs）、十二种深度学习预测模型和97种公开基准配置研究了预测崩溃，发现其与目标可预测性密切相关。我们识别出其背后的两个不同原因：低可预测性限制了校准点预测的幅度，而逐序列目标导致跨序列结构未被识别。这些发现揭示了一种校准-排序权衡：优化平方误差会导致预测平坦，而直接优化横截面相关性虽能改善排序，却可能使预测幅度膨胀一个数量级以上。为解决这一权衡，我们引入了CalibRank——一种平衡校准与排序的简单目标函数。在Finance1K上，CalibRank使横截面相关性几乎增至三倍，同时保持幅度接近目标，并在所有测试模型上提升了相关性。我们的结果揭示了传统时间序列评估的一个盲点：逐序列指标可能掩盖下游决策所需的跨序列结构中的失败。

English

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.