Neural Temporal Point Processes: A Review

Shchur, Oleksandr; Türkmen, Ali Caner; Januschowski, Tim; Günnemann, Stephan

doi:10.24963/ijcai.2021/623

Neural Temporal Point Processes: A Review

Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4585-4593.

https://doi.org/10.24963/ijcai.2021/623

Keywords

Machine learning: General

Abstract

Temporal point processes (TPP) are probabilistic generative models for continuous-time event sequences. Neural TPPs combine the fundamental ideas from point process literature with deep learning approaches, thus enabling construction of flexible and efficient models. The topic of neural TPPs has attracted significant attention in the recent years, leading to the development of numerous new architectures and applications for this class of models. In this review paper we aim to consolidate the existing body of knowledge on neural TPPs. Specifically, we focus on important design choices and general principles for defining neural TPP models. Next, we provide an overview of application areas commonly considered in the literature. We conclude this survey with the list of open challenges and important directions for future work in the field of neural TPPs.

Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, and Stephan Günnemann. 2021. Neural Temporal Point Processes: A Review. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4585–4593. https://doi.org/10.24963/ijcai.2021/623

@inproceedings{ijcai2021p623,
  title     = {Neural Temporal Point Processes: A Review},
  author    = {Shchur, Oleksandr and Türkmen, Ali Caner and Januschowski, Tim and Günnemann, Stephan},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4585--4593},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/623},
  url       = {https://doi.org/10.24963/ijcai.2021/623},
}

TY  - CPAPER
TI  - Neural Temporal Point Processes: A Review
AU  - Shchur, Oleksandr
AU  - Türkmen, Ali Caner
AU  - Januschowski, Tim
AU  - Günnemann, Stephan
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4585
EP  - 4593
DO  - 10.24963/ijcai.2021/623
UR  - https://doi.org/10.24963/ijcai.2021/623
ER  -