Summary of Conformalized Adaptive Forecasting Of Heterogeneous Trajectories, by Yanfei Zhou et al.
Conformalized Adaptive Forecasting of Heterogeneous Trajectories
by Yanfei Zhou, Lars Lindemann, Matteo Sesia
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 The proposed method offers a novel approach to generating simultaneous forecasting bands that guarantee coverage of the entire path of a new random trajectory with high probability. By combining techniques from online conformal prediction of single and multiple time series, as well as addressing heteroscedasticity in regression, this solution provides principled finite-sample guarantees and often leads to more informative predictions than prior methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make accurate forecasts about the future behavior of many different objects moving unpredictably. The method is special because it gives precise estimates of how likely it is that its predictions will be correct. This is important in situations where we need to plan the movements of multiple objects, such as robots or vehicles. |
Keywords
* Artificial intelligence * Probability * Regression * Time series
