Look Back, Correct Forward: Error Feedback Calibration for Time Series Forecasting
To address this, we introduce Error Feedback Calibration (EFC), a fully test-time framework for deterministic post-hoc correction of time series forecasting.
ProofPaper ↗
Key points
- Time series forecasting models are increasingly deployed in continuously evolving environments.
- However, it remains unclear whether verified forecasting errors can themselves provide causal feedback for correcting future predictions.
- EFC constructs a Causal Error Diagonal (CED) from revealed errors of overlapping historical forecasts, providing a fresh view of the frozen forecaster's recent failure behavior.
- Temporal Residual Learning (TRL) further predicts only the residual structure left after CET from recent CED trajectories, rather than relearning the entire forecasting error.
Sources (1)
- [1]Look Back, Correct Forward: Error Feedback Calibration for Time Series ForecastingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 03:22 AM
To address this, we introduce Error Feedback Calibration (EFC), a fully test-time framework for deterministic post-hoc correction of time series forecasting.
Time series forecasting models are increasingly deployed in continuously evolving environments.
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