Reconstruction-based anomaly detection for multivariate time series using contrastive generative adversarial networks

Jiawei Miao; Haicheng Tao; Haoran Xie; Jianshan Sun; Jie Cao

doi:10.1016/j.ipm.2023.103569

ScienceGate Book Chapters

JOURNAL ARTICLE

Reconstruction-based anomaly detection for multivariate time series using contrastive generative adversarial networks

Jiawei Miao Haicheng Tao Haoran Xie Jianshan Sun Jie Cao

Year: 2023 Journal: Information Processing & Management Vol: 61 (1)Pages: 103569-103569 Publisher: Elsevier BV

DOI: 10.1016/j.ipm.2023.103569

Get Full-Text PDF Get Analytical Report

Keywords:

Multivariate statistics Series (stratigraphy) Anomaly detection Generative grammar Adversarial system Anomaly (physics) Artificial intelligence Computer science Pattern recognition (psychology) Machine learning Geology

Metrics

Cited By

15.58

FWCI (Field Weighted Citation Impact)

Refs

0.99

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

Reconstruction-based anomaly detection for multivariate time series using contrastive generative adversarial networks

Metrics

Citation History

Topics

Related Documents

Multimodal Generative Adversarial Networks Based Multivariate Time Series Anomaly Detection

Anomaly Detection in Time Series using Generative Adversarial Networks

Multivariate Time Series Anomaly Detection With Generative Adversarial Networks Based on Active Distortion Transformer

RGAnomaly: Data reconstruction-based generative adversarial networks for multivariate time series anomaly detection in the Internet of Things

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks