DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection

Xiaotong Zhao; Peng Liu; Saïd Mahmoudi; Sahil Garg; Georges Kaddoum; Mohammad Mehedi Hassan

doi:10.1016/j.aej.2024.07.013

ScienceGate Book Chapters

JOURNAL ARTICLE

DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection

Xiaotong Zhao Peng Liu Saïd Mahmoudi Sahil Garg Georges Kaddoum Mohammad Mehedi Hassan

Year: 2024 Journal: Alexandria Engineering Journal Vol: 108 Pages: 436-444 Publisher: Elsevier BV

DOI: 10.1016/j.aej.2024.07.013

Get Full-Text PDF Get Analytical Report

Keywords:

Autoencoder Anomaly detection Computer science Unsupervised learning Artificial intelligence Multivariate statistics Context (archaeology) Machine learning Data mining Feature (linguistics) Time series Feature vector Pattern recognition (psychology) Deep learning Geography

Metrics

Cited By

4.47

FWCI (Field Weighted Citation Impact)

Refs

0.92

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Time Series Analysis and Forecasting

Physical Sciences → Computer Science → Signal Processing

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

DDANF: Deep denoising autoencoder normalizing flow for unsupervised multivariate time series anomaly detection

Metrics

Citation History

Topics

Related Documents

Conditional normalizing flow for multivariate time series anomaly detection

MADFlow : Multivariate Time Series Anomaly Detection via Normalizing Flow

Unsupervised Multi-Head Attention Autoencoder for Multivariate Time-Series Anomaly Detection

Robust Unsupervised Anomaly Detection With Variational Autoencoder in Multivariate Time Series Data

Unsupervised Anomaly Detection in Multivariate Time Series through Transformer-based Variational Autoencoder