A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data

A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data

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...

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Bibliographic Details
Main Authors: Yıldız Karadayı, Mehmet N. Aydin, A. Selçuk Öğrenci
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Applied Sciences
Subjects:
spatio-temporal anomaly detection
unsupervised learning
multivariate data
deep learning
CNN
LSTM
Online Access:https://www.mdpi.com/2076-3417/10/15/5191

Internet

https://www.mdpi.com/2076-3417/10/15/5191

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