Forecast Collapse in Time-Series Foundation Models
- Published
- Source
- arXiv
- Paper number
- 920
- Field
- Machine Learning
- arXiv ID
- 2608.14106
Key points
- Finance1K consists of 28,510 hourly time points for 1,000 U.S. stocks from 2015 through early 2026, and it compares stock returns and volume changes under the same conditions.
- When the target has little predictable signal, as with stock returns, even accurate point forecasts can end up smaller than the actual variation. Training to reduce per-stock error does not directly guarantee the cross-stock ranking at the same time point.
- In the baseline model, MSE training produced a prediction spread that was 0.027 times the actual spread and an information coefficient of 0.046 for stock ranking. CalibRank achieved a prediction spread of 1.836 times and an IC of 0.126.
- Across 12 forecasting models, CalibRank improved the IC of every model. The mean IC increased from 0.062 to 0.123, while MSE changed from 0.00043 to 0.00058.
- The stock-level problem and solution were verified only on Finance1K. CalibRank handles only Pearson correlation, does not model tail risk or the full joint distribution, and cannot be applied directly to a single time series.
Paper links
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