Summary of Kernel-based Optimally Weighted Conformal Prediction Intervals, by Jonghyeok Lee et al.
Kernel-based optimally weighted conformal prediction intervals
by Jonghyeok Lee, Chen Xu, Yao Xie
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); 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 novel conformal prediction method, Kernel-based Optimally Weighted Conformal Prediction Intervals (KOWCPI), adapts the Reweighted Nadaraya-Watson (RNW) estimator for quantile regression on dependent data. It learns optimal data-adaptive weights to establish a conditional coverage guarantee under strong mixing conditions on non-conformity scores. In experiments, KOWCPI outperforms state-of-the-art methods in time-series forecasting, achieving narrower confidence intervals without sacrificing coverage. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Conformal prediction is a way to measure how sure we are about something. This new method, called KOWCPI, helps us do this for time-series data, which is like predicting what will happen next based on past events. It’s special because it can learn from the data and get better at making predictions. The experts tested it and found that it did a great job, giving them more accurate predictions without being too wide or too narrow. |
Keywords
» Artificial intelligence » Regression » Time series
