Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy

dc.contributor.authorAydın, Mehmet Nafiz
dc.contributor.authorAydın, Mehmet Nafiz
dc.contributor.authorÖğrenci, Arif Selçuk
dc.date.accessioned2020-12-11T18:07:10Z
dc.date.available2020-12-11T18:07:10Z
dc.date.issued2020
dc.departmentFakülteler, İşletme Fakültesi, Yönetim Bilişim Sistemleri Bölümüen_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractUnsupervised anomaly detection for spatio-temporal data has extensive use in a wide variety of applications such as earth science, traffic monitoring, fraud and disease outbreak detection. Most real-world time series data have a spatial dimension as an additional context which is often expressed in terms of coordinates of the region of interest (such as latitude - longitude information). However, existing techniques are limited to handle spatial and temporal contextual attributes in an integrated and meaningful way considering both spatial and temporal dependency between observations. In this paper, a hybrid deep learning framework is proposed to solve the unsupervised anomaly detection problem in multivariate spatio-temporal data. The proposed framework works with unlabeled data and no prior knowledge about anomalies are assumed. As a case study, we use the public COVID-19 data provided by the Italian Department of Civil Protection. Northern Italy regions' COVID-19 data are used to train the framework; and then any abnormal trends or upswings in COVID-19 data of central and southern Italian regions are detected. The proposed framework detects early signals of the COVID-19 outbreak in test regions based on the reconstruction error. For performance comparison, we perform a detailed evaluation of 15 algorithms on the COVID-19 Italy dataset including the state-of-the-art deep learning architectures. Experimental results show that our framework shows significant improvement on unsupervised anomaly detection performance even in data scarce and high contamination ratio scenarios (where the ratio of anomalies in the data set is more than 5%). It achieves the earliest detection of COVID-19 outbreak and shows better performance on tracking the peaks of the COVID-19 pandemic in test regions. As the timeliness of detection is quite important in the fight against any outbreak, our framework provides useful insight to suppress the resurgence of local novel coronavirus outbreaks as early as possible.en_US
dc.description.sponsorshipKadir Has Universityen_US
dc.identifier.citation18
dc.identifier.doi10.1109/ACCESS.2020.3022366en_US
dc.identifier.endpage164177en_US
dc.identifier.issn2169-3536en_US
dc.identifier.issn2169-3536
dc.identifier.pmid34931155en_US
dc.identifier.scopus2-s2.0-85096693273en_US
dc.identifier.scopusqualityQ1
dc.identifier.startpage164155en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12469/3514
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2020.3022366
dc.identifier.volume8en_US
dc.identifier.wosWOS:000573031400001en_US
dc.identifier.wosqualityQ2
dc.institutionauthorKaradayı, Yıldızen_US
dc.institutionauthorAydın, Mehmet Nafizen_US
dc.institutionauthorÖǧrenci, Arif Selçuken_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrıcal Electronıcs Engıneers Incen_US
dc.relation.journalIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAnomaly detectionen_US
dc.subjectMachine learningen_US
dc.subjectSpatial databasesen_US
dc.subjectClustering algorithmsen_US
dc.subjectDiseasesen_US
dc.subjectTime series analysisen_US
dc.subjectData modelsen_US
dc.subjectSpatio-temporal anomaly detectionen_US
dc.subjectDeep learningen_US
dc.subjectCOVID-19en_US
dc.subjectOutbreak detectionen_US
dc.subjectItalyen_US
dc.subjectMultivariate dataen_US
dc.subjectUnsuperviseden_US
dc.titleUnsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italyen_US
dc.typeArticleen_US
dspace.entity.typePublication
relation.isAuthorOfPublicationa66a9279-fa0c-4915-816f-40c93cee4747
relation.isAuthorOfPublication.latestForDiscoverya66a9279-fa0c-4915-816f-40c93cee4747

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Unsupervised Anomaly Detection in Multivariate Spatio-Temporal.pdf
Size:
1.79 MB
Format:
Adobe Portable Document Format
Description: