Forecast Collapse in Time-Series Foundation Models

• 类型：arxiv
• 标识：2608.14106
• 链接：https://arxiv.org/abs/2608.14106
• 主分类：evaluation
• 形态：benchmark
• 被引：0
• 被引来源：Semantic Scholar
• S2被引：0
• 影响力被引：0
• TLDR：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 p
• 待LLM分类：否
• 标题中文：时序基础模型中的预测坍缩现象
• TLDR中文：在对 1,000 只美股进行小时级收益预测时，我们观察到一种意外现象：预测结果近乎平坦，且截面相关性衡量的股票排序能力很差，我们将其称为"预测坍缩"。令人惊讶的是，在同一设置下对交易量进行预测时，该现象基本消失。我们在多种时序基础模型（TSFMs）、12 个深度学习预测模型以及 97 个公开基准配置中系统考察了这一现象，发现其与目标可预测性密切相关，并识别出背后的两类成因：低 p……
• 来源文件：
• /inbox/tom/_candidates/2026-08-17-agent-rag-longcontext-candidates.json
• [S2 enrich]