Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learning

dc.contributor.authorKaradayı, Yıldız
dc.date.accessioned2020-12-22T22:01:40Z
dc.date.available2020-12-22T22:01:40Z
dc.date.issued2020
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractTechniques used for spatio-temporal anomaly detection in an unsupervised settings has attracted great attention in recent years. It has extensive use in a wide variety of applications such as: medical diagnosis, sensor events analysis, earth science, fraud detection systems, etc. Most of the real world time series datasets have spatial dimension as additional context such as geographic location. Although many temporal data are spatio-temporal in nature, existing techniques are limited to handle both contextual (spatial and temporal) attributes during anomaly detection process. Taking into account of spatial context in addition to temporal context would help uncovering complex anomaly types and unexpected and interesting knowledge about problem domain. In this paper, a new approach to the problem of unsupervised anomaly detection in a multivariate spatio-temporal dataset is proposed using a hybrid deep learning framework. The proposed approach is composed of a Long Short Term Memory (LSTM) Encoder and Deep Neural Network (DNN) based classifier to extract spatial and temporal contexts. Although the approach has been employed on crime dataset from San Francisco Police Department to detect spatio-temporal anomalies, it can be applied to any spatio-temporal datasets.en_US
dc.identifier.citation0
dc.identifier.doi10.1007/978-3-030-39098-3_13en_US
dc.identifier.endpage182en_US
dc.identifier.issn0302-9743en_US
dc.identifier.issn0302-9743
dc.identifier.scopus2-s2.0-85082124850en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage167en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3633
dc.identifier.urihttps://doi.org/10.1007/978-3-030-39098-3_13
dc.identifier.volume11986en_US
dc.identifier.wosWOS:000655391400013en_US
dc.identifier.wosqualityN/A
dc.institutionauthorKaradayı, Yıldızen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.journalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDeep learningen_US
dc.subjectMultivariateen_US
dc.subjectSpatio-temporal dataen_US
dc.subjectUnsupervised anomaly detectionen_US
dc.titleUnsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learningen_US
dc.typeConference Objecten_US
dspace.entity.typePublication

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