Summary of Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series, by Zitong Yang et al.
Bellman Conformal Inference: Calibrating Prediction Intervals For Time Series
by Zitong Yang, Emmanuel Candès, Lihua Lei
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces Bellman Conformal Inference (BCI), a framework that improves time series forecasting by providing approximately calibrated prediction intervals. BCI wraps around any forecasting model, allowing it to leverage multi-step ahead forecasts and optimize average interval lengths using a one-dimensional stochastic control problem. The authors use dynamic programming to find the optimal policy for this problem, ensuring long-term coverage under distribution shifts and temporal dependence, even with poor forecasts. Empirical results show that BCI produces substantially shorter prediction intervals compared to existing methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to predict what will happen in the future based on past data. It’s called Bellman Conformal Inference (BCI) and it helps us make more accurate predictions by providing better information about how likely different outcomes are. The method works by looking at how well a forecasting model does, and then adjusting its predictions based on that. This makes sure that the predictions are good even when things change or there’s some randomness involved. In tests, BCI did better than other methods in making accurate predictions. |
Keywords
* Artificial intelligence * Inference * Time series
