Is your feature request related to a problem?
TimesFM provides strong zero-shot forecasts, but it can be difficult to determine how confidence degrades as the forecast horizon increases.
In practice, users often trust short-horizon predictions more than long-horizon predictions, yet there is limited visibility into where forecast reliability begins to break down.
Describe the solution you'd like
Add an optional forecast diagnostics report that evaluates prediction confidence across different forecast horizons.
Example:
- Horizon 1–7: High confidence
- Horizon 8–30: Moderate confidence
- Horizon 31–90: Low confidence
Potential metrics:
- Prediction interval width
- Forecast variance growth
- Calibration error by horizon
- Historical backtest performance
Describe alternatives you've considered
Users can manually run rolling backtests, but this requires additional experimentation and implementation effort.
Additional context
This would make TimesFM easier to evaluate in production settings where forecast reliability is often more important than a single aggregate accuracy metric.
Is your feature request related to a problem?
TimesFM provides strong zero-shot forecasts, but it can be difficult to determine how confidence degrades as the forecast horizon increases.
In practice, users often trust short-horizon predictions more than long-horizon predictions, yet there is limited visibility into where forecast reliability begins to break down.
Describe the solution you'd like
Add an optional forecast diagnostics report that evaluates prediction confidence across different forecast horizons.
Example:
Potential metrics:
Describe alternatives you've considered
Users can manually run rolling backtests, but this requires additional experimentation and implementation effort.
Additional context
This would make TimesFM easier to evaluate in production settings where forecast reliability is often more important than a single aggregate accuracy metric.