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dc.contributor.authorKaradayı, Yıldız
dc.contributor.authorAydın, Mehmet Nafiz
dc.contributor.authorÖğrenci, Arif Selçuk
dc.date.accessioned2020-08-14T12:13:41Z
dc.date.available2020-08-14T12:13:41Z
dc.date.issued2020
dc.identifier.issn2076-3417
dc.identifier.urihttps://doi.org/10.3390/app10155191en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3175
dc.description.abstractMultivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.en_US
dc.language.isoEnglishen_US
dc.publisherMDPI AGen_US
dc.subjectCNNen_US
dc.subjectDeep learningen_US
dc.subjectHurricane Katrinaen_US
dc.subjectHurricane trackingen_US
dc.subjectLSTMen_US
dc.subjectMultivariate dataen_US
dc.subjectSpatio-temporal anomaly detectionen_US
dc.subjectUnsupervised learningen_US
dc.titleA hybrid deep learning framework for unsupervised anomaly detection in multivariate spatio-temporal dataen_US
dc.typeArticleen_US
dc.relation.journalApplied Sciencesen_US
dc.identifier.issue15en_US
dc.identifier.volume10en_US
dc.identifier.doi10.3390/app10155191en_US
dc.contributor.khasauthorKaradayı, Yıldız
dc.contributor.khasauthorAydın, Mehmet Nafiz
dc.contributor.khasauthorÖğrenci, Arif Selçuk


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