KANJDP: Interpretable Temporal Point Process Modeling with Kolmogorov–Arnold Representation

Accurate modeling of event sequences is valuable in domains like electronic health records, financial risk management, and social networks. Random time intervals in these sequences contain key dynamic information, and temporal point processes (TPPs) are widely used to analyze event triggering mechan...

詳細記述

書誌詳細

出版年:	Mathematics
主要な著者:	Ziwei Wu, Guangyin Jin, Xueqiang Gu, Chao Wang
フォーマット:	論文
言語:	英語
出版事項:	MDPI AG 2025-08-01
主題:	temporal point processes Kolmogorov–Arnold networks jump-diffusion SDEs event prediction
オンライン･アクセス:	https://www.mdpi.com/2227-7390/13/17/2754