Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines

As industries become automated and connectivity technologies advance, a wide range of systems continues to generate massive amounts of data. Many approaches have been proposed to extract principal indicators from the vast sea of data to represent the entire system state. Detecting anomalies using th...

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Bibliographic Details
Main Authors: Kukjin Choi, Jihun Yi, Changhwa Park, Sungroh Yoon
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Anomaly detection
deep learning
fault diagnosis
industry applications
Internet-of-Things (IoT)
time series analysis
Online Access:https://ieeexplore.ieee.org/document/9523565/

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