時間序列基礎模型中的預測崩潰

摘要

在預測1,000檔美國股票的每小時報酬率時，我們觀察到一個出乎意料的現象：預測結果近乎平坦，且以橫斷面相關性衡量時，股票排序表現不佳。我們將此現象稱為「預測崩潰」。令人驚訝的是，在相同設定下預測交易量時，此現象大幅消失。我們在時間序列基礎模型（TSFMs）、十二種深度學習預測模型及97組公開基準配置中探究預測崩潰現象，發現其與目標可預測性密切相關。我們從中辨識出兩個不同成因：低可預測性限制了校準點預測的幅度，而逐序列的目標函數則未能識別跨序列結構。這些發現揭示了校準與排序之間的權衡：優化均方誤差會導致預測趨於平坦，而直接優化橫斷面相關性雖能改善排序，卻可能使預測幅度膨脹逾一個數量級。為解決此權衡，我們提出CalibRank，一個兼顧校準與排序的簡潔目標函數。在Finance1K上，CalibRank使橫斷面相關性幾乎提升三倍，同時保持預測幅度接近目標值，並在所有受測模型上均提升了相關性。我們的結果揭示了傳統時間序列評估中的一個盲點：逐序列指標可能掩蓋下游決策所需的跨序列結構之失敗。

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.