FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models

FuseAD: Unsupervised Anomaly Detection in Streaming Sensors Data by Fusing Statistical and Deep Learning Models

The need for robust unsupervised anomaly detection in streaming data is increasing rapidly in the current era of smart devices, where enormous data are gathered from numerous sensors. These sensors record the internal state of a machine, the external environment, and the interaction of machines with...

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Bibliographic Details
Main Authors: Mohsin Munir, Shoaib Ahmed Siddiqui, Muhammad Ali Chattha, Andreas Dengel, Sheraz Ahmed
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Sensors
Subjects:
time-series analysis
anomaly detection
deep neural networks
statistical models
model fusion
sensor data
Online Access:https://www.mdpi.com/1424-8220/19/11/2451

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