Neural Spatiotemporal Point Processes: Trends and Challenges
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arXiv
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| Format: | Preprint |
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2025
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| author | Mukherjee, Sumantrak Elhamdi, Mouad Mohler, George Selby, David A. Xie, Yao Vollmer, Sebastian Grossmann, Gerrit |
| author_facet | Mukherjee, Sumantrak Elhamdi, Mouad Mohler, George Selby, David A. Xie, Yao Vollmer, Sebastian Grossmann, Gerrit |
| contents | Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating modern deep learning techniques, STPPs can model these complexities more effectively than traditional approaches. Consequently, the fusion of neural methods with STPPs has become an active and rapidly evolving research area. In this review, we categorize existing approaches, unify key design choices, and explain the challenges of working with this data modality. We further highlight emerging trends and diverse application domains. Finally, we identify open challenges and gaps in the literature. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09341 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Neural Spatiotemporal Point Processes: Trends and Challenges Mukherjee, Sumantrak Elhamdi, Mouad Mohler, George Selby, David A. Xie, Yao Vollmer, Sebastian Grossmann, Gerrit Machine Learning Artificial Intelligence Spatiotemporal point processes (STPPs) are probabilistic models for events occurring in continuous space and time. Real-world event data often exhibit intricate dependencies and heterogeneous dynamics. By incorporating modern deep learning techniques, STPPs can model these complexities more effectively than traditional approaches. Consequently, the fusion of neural methods with STPPs has become an active and rapidly evolving research area. In this review, we categorize existing approaches, unify key design choices, and explain the challenges of working with this data modality. We further highlight emerging trends and diverse application domains. Finally, we identify open challenges and gaps in the literature. |
| title | Neural Spatiotemporal Point Processes: Trends and Challenges |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.09341 |