Spatiotemporal sequence prediction with point processes and self-organizing decision trees
buir.contributor.author | Karaahmetoğlu, Oğuzhan | |
buir.contributor.author | Serdar Kozat, Süleyman | |
buir.contributor.orcid | Karaahmetoğlu, Oğuzhan|0000-0002-0131-6782 | |
buir.contributor.orcid | Serdar Kozat, Süleyman|0000-0002-6488-3848 | |
dc.citation.epage | 3110 | en_US |
dc.citation.issueNumber | 6 | |
dc.citation.spage | 3097 | en_US |
dc.citation.volumeNumber | 34 | |
dc.contributor.author | Karaahmetoğlu, Oğuzhan | |
dc.contributor.author | Serdar Kozat, Süleyman | en_US |
dc.date.accessioned | 2022-03-04T13:19:24Z | |
dc.date.available | 2022-03-04T13:19:24Z | |
dc.date.issued | 2021-09-22 | |
dc.department | Department of Electrical and Electronics Engineering | en_US |
dc.description.abstract | We study the spatiotemporal prediction problem and introduce a novel point-process-based prediction algorithm. Spatiotemporal prediction is extensively studied in machine learning literature due to its critical real-life applications, such as crime, earthquake, and social event prediction. Despite these thorough studies, specific problems inherent to the application domain are not yet fully explored. Here, we address the nonstationary spatiotemporal prediction problem on both densely and sparsely distributed sequences. We introduce a probabilistic approach that partitions the spatial domain into subregions and models the event arrivals in each region with interacting point processes. Our algorithm can jointly learn the spatial partitioning and the interaction between these regions through a gradient-based optimization procedure. Finally, we demonstrate the performance of our algorithm on both simulated data and two real-life datasets. We compare our approach with baseline and state-of-the-art deep learning-based approaches, where we achieve significant performance improvements. Moreover, we also show the effect of using different parameters on the overall performance through empirical results and explain the procedure for choosing the parameters. | en_US |
dc.description.provenance | Submitted by Dilan Ayverdi (dilan.ayverdi@bilkent.edu.tr) on 2022-03-04T13:19:24Z No. of bitstreams: 1 Spatiotemporal_sequence_prediction_with_point_processes_and_self-organizing_decision_trees.pdf: 1744467 bytes, checksum: 3a6c435c3910332234f64deffb61d9e8 (MD5) | en |
dc.description.provenance | Made available in DSpace on 2022-03-04T13:19:24Z (GMT). No. of bitstreams: 1 Spatiotemporal_sequence_prediction_with_point_processes_and_self-organizing_decision_trees.pdf: 1744467 bytes, checksum: 3a6c435c3910332234f64deffb61d9e8 (MD5) Previous issue date: 2021-09-22 | en |
dc.identifier.doi | 10.1109/TNNLS.2021.3111817 | en_US |
dc.identifier.eissn | 2162-2388 | |
dc.identifier.issn | 2162-237X | |
dc.identifier.uri | http://hdl.handle.net/11693/77686 | |
dc.language.iso | English | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.relation.isversionof | https://doi.org/10.1109/TNNLS.2021.3111817 | en_US |
dc.source.title | IEEE Transactions on Neural Networks and Learning Systems | en_US |
dc.subject | Adaptive decision trees | en_US |
dc.subject | Crime prediction | en_US |
dc.subject | Earthquake prediction | en_US |
dc.subject | Hawkes process | en_US |
dc.subject | Nonstationary time-series data | en_US |
dc.subject | Online learning | en_US |
dc.subject | Spatiotemporal point process | en_US |
dc.title | Spatiotemporal sequence prediction with point processes and self-organizing decision trees | en_US |
dc.type | Article | en_US |
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