Modeling of spatio-temporal hawkes processes with randomized kernels

Simple item page
buir.contributor.authorİlhan, Fatih
buir.contributor.authorKozat, Süleyman Serdar
dc.citation.epage4958en_US
dc.citation.spage4946en_US
dc.citation.volumeNumber68en_US
dc.contributor.authorİlhan, Fatih
dc.contributor.authorKozat, Süleyman Serdar
dc.date.accessioned2021-03-17T11:32:01Z
dc.date.available2021-03-17T11:32:01Z
dc.date.issued2020
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractWe investigate spatio-temporal event analysis using point processes. Inferring the dynamics of event sequences spatio-temporally has many practical applications including crime prediction, social media analysis, and traffic forecasting. In particular, we focus on spatio-temporal Hawkes processes that are commonly used due to their capability to capture excitations between event occurrences. We introduce a novel inference framework based on randomized transformations and gradient descent to learn the process. We replace the spatial kernel calculations by randomized Fourier feature-based transformations. The introduced randomization by this representation provides flexibility while modeling the spatial excitation between events. Moreover, the system described by the process is expressed within closed-form in terms of scalable matrix operations. During the optimization, we use maximum likelihood estimation approach and gradient descent while properly handling positivity and orthonormality constraints. The experiment results show the improvements achieved by the introduced method in terms of fitting capability in synthetic and real-life datasets with respect to the conventional inference methods in the spatio-temporal Hawkes process literature. We also analyze the triggering interactions between event types and how their dynamics change in space and time through the interpretation of learned parameters.en_US
dc.description.provenanceSubmitted by Onur Emek (onur.emek@bilkent.edu.tr) on 2021-03-17T11:32:01Z No. of bitstreams: 1 Modeling_of_Spatio-Temporal_Hawkes_Processes_With_Randomized_Kernels.pdf: 2047669 bytes, checksum: a92165f7aedbf9001a182db966e2ce35 (MD5)en
dc.description.provenanceMade available in DSpace on 2021-03-17T11:32:01Z (GMT). No. of bitstreams: 1 Modeling_of_Spatio-Temporal_Hawkes_Processes_With_Randomized_Kernels.pdf: 2047669 bytes, checksum: a92165f7aedbf9001a182db966e2ce35 (MD5) Previous issue date: 2020en
dc.description.sponsorshipThis work was supported in part by Tubitak Contract No: 117E153.en_US
dc.identifier.doi10.1109/TSP.2020.3019329en_US
dc.identifier.issn1053-587X
dc.identifier.urihttp://hdl.handle.net/11693/75949
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/TSP.2020.3019329en_US
dc.source.titleIEEE Transactions on Signal Processingen_US
dc.subjectParameter estimationen_US
dc.subjectTime seriesen_US
dc.subjectSystem modelingen_US
dc.subjectPoint processesen_US
dc.subjectRandom Fourier featuresen_US
dc.subjectEvent analysisen_US
dc.titleModeling of spatio-temporal hawkes processes with randomized kernelsen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Modeling_of_Spatio-Temporal_Hawkes_Processes_With_Randomized_Kernels.pdf
Size:
1.95 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Scholarly Publications - Electrical and Electronics Engineering