Recommended Reading on Temporal Point Process (TPP)

Mainly Contributed by Chenghao Yang.

Thanks to Hongyuan Mei and Jason Eisner for providing support and helpful comments.

Thanks for all great contributors on GitHub!

Contents

• 0. Toolkits
• 1. Survey Papers
• 2. Modeling Papers
  • 2.1 Temporal Point Process Modeling
  • 2.2 Structured Temporal Point Process Modeling
• 3. Algorithm Papers
  • 3.1 Training Algorithm
  • 3.2 Imputing
  • 3.3 Anomaly Detection
  • 3.4 Clustering
  • 3.5 Data Augmentation
  • 3.6 Denoising
  • 3.7 Querying
• 4. Application Papers
  • 4.1 Social Media
  • 4.2 Clinical Health
  • 4.3 E-commerce
  • 4.4 Causality Discovery
  • 4.5 Audio Processing
  • 4.6 Natural Language Processing
  • 4.7 Computer Vision
  • 4.8 Network Structure Discovery
  • 4.9 Science
  • 4.10 Reinforcement Learning
• 5. Benchmark
• 6. Research Opportunities
• Acknowledgements

0. Toolkits

1. EasyTPP: Towards Open Benchmarking the Temporal Point Processes. Siqiao Xue, Xiaoming Shi, Zhixuan Chu, Yan Wang, Fan Zhou, Hongyan Hao, Caigao Jiang, Chen Pan, Yi Xu, James Y. Zhang, Qingsong Wen, Jun Zhou, Hongyuan Mei. ICLR 2024. [pdf] [code]
2. TPPToolkits: Toolkits for Temporal Point Process. Hongyuan Mei, Chenghao Yang. [website]
3. PoPPy: A Point Process Toolbox Based on PyTorch. Hongteng Xu. arXiv 2018. [website] [pdf]
4. THAP: A Matlab Toolkit for Learning with Hawkes Processes. Hongteng Xu, Hongyuan Zha. arXiv 2017. [website] [pdf]
5. Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling. Emmanuel Bacry, Martin Bompaire, Stéphane Gaïffas, Soren Poulsen. arXiv 2017. [website] [pdf]

1. Survey Papers

1. Transformers in Time Series: A Survey. Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, Liang Sun. IJCAI 2023. [pdf]
2. Neural Temporal Point Processes: A Review. Oleksandr Shchur, Ali Caner Türkmen, Tim Januschowski, Stephan Günnemann. IJCAI 2021. [pdf]
3. Recent Advance in Temporal Point Process: from Machine Learning Perspective. Junchi Yan. SJTU Technical Report 2019. [pdf]

2. Modeling Papers

2.1 Temporal Point Process Modeling

1. Neural Jump-Diffusion Temporal Point Processes. Shuai Zhang, Chuan Zhou, Yang Aron Liu, Peng Zhang, Xixun Lin, Zhi-Ming Ma. ICML 2024. [paper] [code]
2. Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning. Xiaoming Shi, Siqiao Xue, Kangrui Wang, Fan Zhou, James Y. Zhang, Jun Zhou, Chenhao Tan, Hongyuan Mei. NeurIPS 2023. [paper] [code]
3. Prompt-augmented Temporal Point Process for Streaming Event Sequence. Siqiao Xue, Yan Wang, Zhixuan Chu, Xiaoming Shi, Caigao Jiang, Hongyan Hao, Gangwei Jiang, Xiaoyun Feng, James Y. Zhang, Jun Zhou. NeurIPS 2023. [paper] [code]
4. Integration-free Training for Spatio-temporal Multimodal Covariate Deep Kernel Point Processes. Yixuan Zhang, Quyu Kong, Feng Zhou. NeurIPS 2023. [paper]
5. Sparse Transformer Hawkes Process for Long Event Sequences. Zhuoqun Li, Mingxuan Sun. ECML-PKDD 2023. [Springer]
6. Intensity-free Convolutional Temporal Point Process: Incorporating Local and Global Event Contexts. Wang-Tao Zhou, Zhao Kang, Ling Tian, Yi Su. Information Sciences 2023. [pdf]
7. Meta Temporal Point Processes. Wonho Bae, Mohamed Osama Ahmed, Frederick Tung, Gabriel L. Oliveira. ICLR 2023. [pdf]
8. HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences. Siqiao Xue, Xiaoming Shi, James Y Zhang, Hongyuan Mei. NeurIPS 2022. [pdf] [code (iLampard)] [code (alipay)]
9. Exploring Generative Neural Temporal Point Process. Haitao Lin, Lirong Wu, Guojiang Zhao, Pai Liu, Stan Z. Li. TMLR 2022. [pdf] [code]
10. Transformer Embeddings of Irregularly Spaced Events and Their Participants. Chenghao Yang, Hongyuan Mei, Jason Eisner. ICLR 2022. [pdf] [code]
11. Long Horizon Forecasting with Temporal Point Processes. Prathamesh Deshpande, Kamlesh Marathe, Abir De, Sunita Sarawagi. WSDM 2021. [pdf]
12. Deep Fourier Kernel for Self-Attentive Point Processes. Shixiang Zhu, Minghe Zhang, Ruyi Ding, Yao Xie. AISTATS 2021. [pdf]
13. Neural Spatio-Temporal Point Processes. Ricky T. Q. Chen, Brandon Amos, Maximilian Nickel. ICLR 2021. [pdf] [code]
14. Transformer Hawkes Process. Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, Hongyuan Zha. ICML 2020. [pdf] [code]
15. Self-Attentive Hawkes Process. Qiang Zhang, Aldo Lipani, Omer Kirnap, Emine Yilmaz. ICML 2020. [pdf] [code]
16. Intensity-Free Learning of Temporal Point Processes. Oleksandr Shchur, Marin Biloš, Stephan Günnemann. ICLR 2020. [pdf] [code]
17. Fast and Flexible Temporal Point Processes with Triangular Maps. Oleksandr Shchur, Nicholas Gao, Marin Biloš, Stephan Günnemann. NeurIPS 2020. [pdf] [code + data]
18. Uncertainty on Asynchronous Time Event Prediction. Marin Biloš, Bertrand Charpentier, Stephan Günnemann. NeurIPS 2019. [pdf] [code]
19. Latent ODEs for Irregularly-Sampled Time Series. Yulia Rubanova, Ricky T. Q. Chen, David Duvenaud. NeurIPS 2019. [pdf] [code]
20. Neural Jump Stochastic Differential Equations. Junteng Jia, Austin R. Benson. NeurIPS 2019. [pdf] [code]
21. Fully Neural Network based Model for General Temporal Point Processes. Takahiro Omi, Naonori Ueda, Kazuyuki Aihara. NeurIPS 2019. [pdf] [code]
22. Deep Reinforcement Learning of Marked Temporal Point Processes. Utkarsh Upadhyay, Abir De, Manuel Gomez-Rodriguez. NeurIPS 2018. [pdf] [code]
23. Learning Conditional Generative Models for Temporal Point Processes. Shuai Xiao, Hongteng Xu, Junchi Yan, Mehrdad Farajtabar, Xiaokang Yang, Le Song, Hongyuan Zha. AAAI 2018. [pdf]
24. The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process. Hongyuan Mei, Jason Eisner. NeurIPS 2017. [pdf] [code] [spotlight]
25. Wasserstein Learning of Deep Generative Point Process Models. Shuai Xiao, Mehrdad Farajtabar, Xiaojing Ye, Junchi Yan, Le Song, Hongyuan Zha. NeurIPS 2017. [pdf] [code]
26. Cascade Dynamics Modeling with Attention-based Recurrent Neural Network. Yongqing Wang, Huawei Shen, Shenghua Liu, Jinhua Gao, Xueqi Cheng. IJCAI 2017. [pdf]
27. Modeling The Intensity Function Of Point Process Via Recurrent Neural Networks. *Shuai Xiao, Junchi Yan, Stephe