research-article

Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes

Authors:

Govind Waghmare,

Siddhartha Asthana,

Aakarsh MalhotraAuthors Info & Claims

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

Pages 1986 - 1995

https://doi.org/10.1145/3511808.3557399

Published: 17 October 2022 Publication History

Abstract

Temporal Point Processes (TPP) are probabilistic generative frameworks. They model discrete event sequences localized in continuous time. Generally, real-life events reveal descriptive information, known as marks. Marked TPPs model time and marks of the event together for practical relevance. Conditioned on past events, marked TPPs aim to learn the joint distribution of the time and the mark of the next event. For simplicity, conditionally independent TPP models assume time and marks are independent given event history. They factorize the conditional joint distribution of time and mark into the product of individual conditional distributions. This structural limitation in the design of TPP models hurt the predictive performance on entangled time and mark interactions. In this work, we model the conditional inter-dependence of time and mark to overcome the limitations of conditionally independent models. We construct a multivariate TPP conditioning the time distribution on the current event mark in addition to past events. Besides the conventional intensity-based models for conditional joint distribution, we also draw on flexible intensity-free TPP models from the literature. The proposed TPP models outperform conditionally independent and dependent models in standard prediction tasks. Our experimentation on various datasets with multiple evaluation metrics highlights the merit of the proposed approach.

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Cited By

Liu B(2024)Cumulative Hazard Function Based Efficient Multivariate Temporal Point Process Learning2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650460(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650460
Liu B(2024)Modelling event sequence data by type-wise neural point processData Mining and Knowledge Discovery10.1007/s10618-024-01047-638:6(3449-3472)Online publication date: 17-Jun-2024
https://doi.org/10.1007/s10618-024-01047-6

Index Terms

Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Location based services

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cover image ACM Conferences

CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management

October 2022

5274 pages

ISBN:9781450392365

DOI:10.1145/3511808

General Chairs:
Mohammad Al Hasan
Indiana University Purdue University, Indianapolis, USA
,
Li Xiong
Emory University, Atlanta, USA

Copyright © 2022 ACM.

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Published: 17 October 2022

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Cited By

Liu B(2024)Cumulative Hazard Function Based Efficient Multivariate Temporal Point Process Learning2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650460(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650460
Liu B(2024)Modelling event sequence data by type-wise neural point processData Mining and Knowledge Discovery10.1007/s10618-024-01047-638:6(3449-3472)Online publication date: 17-Jun-2024
https://doi.org/10.1007/s10618-024-01047-6

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