Summary of Conformal Prediction For Hierarchical Data, by Guillaume Principato et al.
Conformal Prediction for Hierarchical Data
by Guillaume Principato, Gilles Stoltz, Yvenn Amara-Ouali, Yannig Goude, Bachir Hamrouche, Jean-Michel Poggi
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP)
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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 A novel approach to conformal prediction is proposed for multivariate data series with hierarchical structures. By incorporating a projection step into the split conformal prediction procedure, globally smaller prediction regions can be obtained while maintaining the desired coverage levels. Theoretical findings are supported by experiments on both artificial and real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to predict what might happen in a complex system, like a city’s traffic or weather patterns. This paper shows how to do this better by taking into account the relationships between different parts of the system. They develop a new way to make predictions that are more accurate and include less uncertainty. The results are tested on both fake and real data. |
