A Generative Model for Anomaly Detection in Time Series Data

Maximilian Hoh; Alfred Schöttl; Henry Schaub; Franz Wenninger

doi:10.1016/j.procs.2022.01.261

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JOURNAL ARTICLE

A Generative Model for Anomaly Detection in Time Series Data

Maximilian Hoh Alfred Schöttl Henry Schaub Franz Wenninger

Year: 2022 Journal: Procedia Computer Science Vol: 200 Pages: 629-637 Publisher: Elsevier BV

DOI: 10.1016/j.procs.2022.01.261

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Abstract

629

Keywords:

Computer science Regularization (linguistics) Generative grammar Encoder Anomaly detection Code (set theory) Generative model Artificial intelligence Machine learning Representation (politics) Series (stratigraphy) Generator (circuit theory) Pattern recognition (psychology) Data mining External Data Representation

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0.87

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Topics

Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Digital Media Forensic Detection

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Generative Adversarial Networks and Image Synthesis

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

A Generative Model for Anomaly Detection in Time Series Data

Abstract

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Citation History

Topics

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