Modeling non-stationary dynamics of spatio-temporal sequences with self-organizing point process models
| buir.advisor | Kozat, Süleyman Serdar | |
| dc.contributor.author | Karaahmetoğlu, Oğuzhan | |
| dc.date.accessioned | 2021-08-04T13:21:07Z | |
| dc.date.available | 2021-08-04T13:21:07Z | |
| dc.date.copyright | 2021-06 | |
| dc.date.issued | 2021-06 | |
| dc.date.submitted | 2021-07-08 | |
| dc.description | Cataloged from PDF version of article. | en_US |
| dc.description | Thesis (Master's): Bilkent University, Department of Electrical and Electronics Engineering, İhsan Doğramacı Bilkent University, 2021. | en_US |
| dc.description | Includes bibliographical references (leaves 45-49). | en_US |
| dc.description.abstract | We 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.provenance | Submitted by Betül Özen (ozen@bilkent.edu.tr) on 2021-08-04T13:21:07Z No. of bitstreams: 1 10404085.pdf: 1383994 bytes, checksum: 5c2db31d1be0b5b61f92ceee55bb2f45 (MD5) | en |
| dc.description.provenance | Made available in DSpace on 2021-08-04T13:21:07Z (GMT). No. of bitstreams: 1 10404085.pdf: 1383994 bytes, checksum: 5c2db31d1be0b5b61f92ceee55bb2f45 (MD5) Previous issue date: 2021-06 | en |
| dc.description.statementofresponsibility | by Oğuzhan Karaahmetoğlu | en_US |
| dc.format.extent | xii, 49 leaves ; 30 cm. | en_US |
| dc.identifier.itemid | B154479 | |
| dc.identifier.uri | http://hdl.handle.net/11693/76405 | |
| dc.language.iso | English | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | Spatiotemporal modeling | en_US |
| dc.subject | Non-stationary sequence | en_US |
| dc.subject | Time-series forecasting | en_US |
| dc.subject | Point processes | en_US |
| dc.subject | Decision trees | en_US |
| dc.title | Modeling non-stationary dynamics of spatio-temporal sequences with self-organizing point process models | en_US |
| dc.title.alternative | Kendini düzenleyen noktasal süreç modelleri ile uzay-zamansal dizilerin durağan olmayan dinamiklerini modelleme | en_US |
| dc.type | Thesis | en_US |
| thesis.degree.discipline | Electrical and Electronic Engineering | |
| thesis.degree.grantor | Bilkent University | |
| thesis.degree.level | Master's | |
| thesis.degree.name | MS (Master of Science) |