Context-aware spatio-temporal event prediction via convolutional Hawkes processes

DOI10.1007/S10994-022-06136-5OpenAlexW4220725658MaRDI QIDQ2102352

Tomoharu Iwata, Hisashi Kashima, Takeshi Kurashima, Maya Okawa, Hiroyuki Toda, Yusuke Tanaka

Publication date: 28 November 2022

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-022-06136-5

zbMATH Keywords

point processHawkes processdeep learningevent prediction

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• TensorFlow
• Keras
• Adam
• Tensor2Tensor
• StFinMetrics
• SchNet

Cites Work

• Unnamed Item
• Space-time point-process models for earthquake occurrences
• A review of self-exciting spatio-temporal point processes and their applications
• Self-exciting point processes: infections and implementations
• Predicting citywide crowd flows using deep spatio-temporal residual networks
• Self-exciting hurdle models for terrorist activity
• Modelling Heterogeneous Space–Time Occurrences of Earthquakes and its Residual Analysis
• Stochastic Declustering of Space-Time Earthquake Occurrences
• Self-Exciting Point Process Modeling of Crime
• Spectra of some self-exciting and mutually exciting point processes

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