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dc.contributor.advisorKozat, Süleyman Serdar
dc.contributor.authorKaraahmetoğlu, Oğuzhan
dc.date.accessioned2021-08-04T13:21:07Z
dc.date.available2021-08-04T13:21:07Z
dc.date.copyright2021-06
dc.date.issued2021-06
dc.date.submitted2021-07-08
dc.identifier.urihttp://hdl.handle.net/11693/76405
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (Master's): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2021.en_US
dc.descriptionIncludes bibliographical references (leaves 45-49).en_US
dc.description.abstractWe investigate the challenging problem of modeling the non-stationary dynam-ics of spatio-temporal sequences for prediction applications. Spatio-temporal se-quence modeling has critical real-life applications such as natural disaster, social, and criminal event prediction. Even though this problem has been thoroughly studied, many approaches do not address the non-stationarity and sparsity of the spatio-temporal sequences, which are frequently observed in real-life sequences. Here, we introduce a novel prediction algorithm that is capable of modeling non-stationarity in both time and space. Moreover, our algorithm can model both densely and sparsely populated sequences. We partition the spatial region with a decision tree, where each node of the tree corresponds to a subregion. We model the event occurrences in di˙erent subregions in space with individual but inter-acting point processes. Our algorithm can jointly optimize the partitioning tree and the interacting point processes through a gradient-based optimization. We compare our approach with statistical models, probabilistic approaches, and deep learning based approaches, and show that our model achieves the best forecasting performance on real-life datasets such as earthquake and criminal event records.en_US
dc.description.statementofresponsibilityby Oğuzhan Karaahmetoğluen_US
dc.format.extentxii, 49 leaves ; 30 cm.en_US
dc.language.isoEnglishen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSpatiotemporal modelingen_US
dc.subjectNon-stationary sequenceen_US
dc.subjectTime-series forecastingen_US
dc.subjectPoint processesen_US
dc.subjectDecision treesen_US
dc.titleModeling non-stationary dynamics of spatio-temporal sequences with self-organizing point process modelsen_US
dc.title.alternativeKendini düzenleyen noktasal süreç modelleri ile uzay-zamansal dizilerin durağan olmayan dinamiklerini modellemeen_US
dc.typeThesisen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.publisherBilkent Universityen_US
dc.description.degreeM.S.en_US
dc.identifier.itemidB154479


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