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

dc.contributor.author Karadayı, Yıldız
dc.contributor.author Aydın, Mehmet Nafiz
dc.contributor.author Aydın, Mehmet Nafiz
dc.contributor.author Öğrenci, Arif Selçuk
dc.contributor.other Management Information Systems
dc.date.accessioned 2020-12-11T18:07:10Z
dc.date.available 2020-12-11T18:07:10Z
dc.date.issued 2020
dc.department Fakülteler, İşletme Fakültesi, Yönetim Bilişim Sistemleri Bölümü en_US
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü en_US
dc.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü en_US
dc.description.abstract Unsupervised 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.sponsorship Kadir Has University en_US
dc.identifier.citationcount 18
dc.identifier.doi 10.1109/ACCESS.2020.3022366 en_US
dc.identifier.endpage 164177 en_US
dc.identifier.issn 2169-3536 en_US
dc.identifier.issn 2169-3536
dc.identifier.pmid 34931155 en_US
dc.identifier.scopus 2-s2.0-85096693273 en_US
dc.identifier.scopusquality Q1
dc.identifier.startpage 164155 en_US
dc.identifier.uri https://hdl.handle.net/20.500.12469/3514
dc.identifier.uri https://doi.org/10.1109/ACCESS.2020.3022366
dc.identifier.volume 8 en_US
dc.identifier.wos WOS:000573031400001 en_US
dc.identifier.wosquality Q2
dc.institutionauthor Karadayı, Yıldız en_US
dc.institutionauthor Aydın, Mehmet Nafiz en_US
dc.institutionauthor Öǧrenci, Arif Selçuk en_US
dc.language.iso en en_US
dc.publisher Ieee-Inst Electrıcal Electronıcs Engıneers Inc en_US
dc.relation.journal Ieee Access en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 38
dc.subject Anomaly detection en_US
dc.subject Machine learning en_US
dc.subject Spatial databases en_US
dc.subject Clustering algorithms en_US
dc.subject Diseases en_US
dc.subject Time series analysis en_US
dc.subject Data models en_US
dc.subject Spatio-temporal anomaly detection en_US
dc.subject Deep learning en_US
dc.subject COVID-19 en_US
dc.subject Outbreak detection en_US
dc.subject Italy en_US
dc.subject Multivariate data en_US
dc.subject Unsupervised en_US
dc.title Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of Covid-19 Outbreak in Italy en_US
dc.type Article en_US
dc.wos.citedbyCount 24
dspace.entity.type Publication
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