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Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes

MBZUAI · Notable

Research Machine Learning Time Series Uncertainty Quantification KAUST

Summary

A presentation will demonstrate the construction of well-calibrated, distribution-free neural Temporal Point Process (TPP) models from multiple event sequences using conformal prediction. The method builds a distribution-free joint prediction region for event arrival time and type with a finite-sample coverage guarantee. The refined method is based on the highest density regions, derived from the joint predictive density of event arrival time and type to address the challenge of creating a joint prediction region for a bivariate response that includes both continuous and discrete data types. Why it matters: This research from a KAUST postdoc improves uncertainty quantification in neural TPPs, which are crucial for modeling continuous-time event sequences, with applications in various fields, by providing more reliable prediction regions.

Keywords

Temporal Point Processes · conformal prediction · neural networks · uncertainty quantification · event sequences

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