DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series

Traditional distance and density-based anomaly detection techniques are unable to detect periodic and seasonality related point anomalies which occur commonly in streaming data, leaving a big gap in time series anomaly detection in the current era of the IoT. To address this problem, we present a no...

Full description

Bibliographic Details
Main Authors: Mohsin Munir, Shoaib Ahmed Siddiqui, Andreas Dengel, Sheraz Ahmed
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Anomaly detection
artificial intelligence
convolutional neural network
deep neural networks
recurrent neural networks
time series analysis
Online Access:https://ieeexplore.ieee.org/document/8581424/

Internet

https://ieeexplore.ieee.org/document/8581424/

Similar Items