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cloud.google.com·8h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting
TL;DR
RATL introduces a novel approach for multivariate time-series forecasting by utilizing historical forecast residuals as a memory resource, enhancing the performance of base forecasters. This method demonstrates significant improvements in forecasting accuracy across various experimental settings.
✦ Why It Matters
Implement RATL to enhance your time-series forecasting models using historical residuals for better accuracy.
Key Takeaways
- Utilize RATL to improve forecasting accuracy in your models.
- Incorporate historical residuals as a memory resource for better predictions.
- Experiment with learned routing to enhance residual feedback.
- Test RATL with different base forecasters for optimal performance.
How It Works
RATL freezes a base forecaster to create retrieval keys from its historical forecast residuals. At inference, it retrieves similar residual trajectories based on causal availability, using a set-aware router to select and combine these trajectories effectively.