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

Ziwei Wu, Guangyin Jin, Xueqiang Gu () and Chao Wang ()
Additional contact information
Ziwei Wu: College of Intelligence Science and Technology, National University of Defense Technology, No. 109 Deya Street, Changsha 410073, China
Guangyin Jin: National Innovative Institute of Defense Technology, Academy of Military Sciences, No. 53, Dongdajie Street, Beijing 100071, China
Xueqiang Gu: College of Intelligence Science and Technology, National University of Defense Technology, No. 109 Deya Street, Changsha 410073, China
Chao Wang: College of Intelligence Science and Technology, National University of Defense Technology, No. 109 Deya Street, Changsha 410073, China

Mathematics, 2025, vol. 13, issue 17, 1-17

Abstract: 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 mechanisms and probability evolution patterns in asynchronous sequences. Neural TPPs (NTPPs) enhanced by deep learning improve modeling capabilities, but most suffer from limited interpretability due to predefined functional structures. This study proposes KANJDP (Kolmogorov–Arnold Neural Jump-Diffusion Process), a novel event sequence modeling method: it decomposes the intensity function via stochastic differential equations (SDEs), with each component parameterized by learnable spline functions. By analyzing each component’s contribution to event occurrence, KANJDP quantitatively reveals core event generation mechanisms. Experiments on real-world and synthetic datasets show that KANJDP achieves higher prediction accuracy with fewer trainable parameters.

Keywords: temporal point processes; Kolmogorov–Arnold networks; jump-diffusion SDEs; event prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/17/2754/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/17/2754/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:17:p:2754-:d:1733649

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().