Browsing by Author "Karadayı, Yıldız"
Now showing items 1-4 of 4
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A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data
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Publisher and Date:(Mdpi, 2020)Multivariate 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 ...
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A hybrid deep learning framework for unsupervised anomaly detection in multivariate spatio-temporal data / Uzamsal-zamansal çok boyutlu verilerde denetimsiz anomali tespiti için hibrit derin öğrenme çerçevesi
Authors:
Publisher and Date:(Kadir Has Üniversitesi, 2020)Uzamsal-zamansal veriler için denetlenmeyen anormallik tespiti, yer bilimi, trafik izleme, dolandırıcılık ve hastalık salgını tespiti gibi çok çeşitli uygulamalarda yaygın kullanıma sahiptir. Çoğu gerçek dünya zaman serisi verisi, genellikle ilgilenilen bölgenin koordinatları (enlem - boylam bilgisi gibi) cinsinden ifade edilen ek bir bağlam olarak bir uzamsal boyuta sahiptir. Bununla birlikte, mevcut teknikler, gözlemler arasındaki hem uzamsal hem de zamansal bağımlılığı göz önünde bulundurarak, ...
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Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data Using Deep Learning: Early Detection of COVID-19 Outbreak in Italy
Authors:
Publisher and Date:(Ieee-Inst Electrıcal Electronıcs Engıneers Inc, 2020)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 ...
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Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Datasets Using Deep Learning
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Publisher and Date:(Springer, 2020)Techniques 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 ...
