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cloud.google.com·7h ago
AI Research
arXiv CS.AI·28d ago·5 min read
Explaining Time Series Forecasting with Horizon-Resolved Attribution
TL;DR
Horizon-Resolved eXplanation (HRX) enhances time series forecasting by providing distinct importance maps for each forecast step, revealing that different predictions rely on different past values. This method improves interpretability and accuracy in forecasting models.
✦ Why It Matters
Implement HRX in your forecasting models to improve interpretability and accuracy of predictions today.
Key Takeaways
- Adopt HRX to provide tailored explanations for each forecast step.
- Utilize the evaluation protocol to validate importance rankings.
- Leverage shared maps to reduce complexity in explanations.
- Test HRX across different datasets to assess its effectiveness.
How It Works
HRX enhances time series forecasting by introducing a horizon axis that allows each forecast step to have its own importance map. This is achieved through an estimator that extracts these maps from any differentiable forecaster without altering the underlying model.
The evaluation protocol measures the impact of removing inputs ranked highest by the importance maps, validating their significance. Additionally, a rank criterion helps determine when it is beneficial to resolve the horizon axis for a given time series.