EventFormer: a hierarchical neural point process framework for spatio-temporal clustering events prediction
Abstract In real-world scenarios, event data often exhibits inherent randomness, complex historical dependencies, and hierarchical spatio-temporal clustering. However, existing neural point process models typically overlook the hierarchical nature of spatial information, or treat temporal and spatia...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2025-07-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-025-01232-4 |
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