Deep Autoencoding One-Class time Series Anomaly Detection

Time-series Anomaly Detection(AD) is widely used in monitoring and security applications in various industries and has become a hot spot in the field of deep learning. Normality-representation-based methods perform well in certain scenarios but may ignore some aspects of the overall normality. Featu...

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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
Main Authors: Mou, Xudong, Wang, Rui, Wang, Tiejun, Sun, Jie, Li, Bo, Wo, Tianyu, Liu, Xudong
Format: Conference Proceeding
Language:English
Published: IEEE 04.06.2023
Subjects:
Autoen-coder
Deep learning
Industries
Linear programming
Merging
One-class classification
Security
Signal processing
Time series analysis
Time Series Anomaly Detection
ISSN:2379-190X
Online Access:Get full text
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Summary:Time-series Anomaly Detection(AD) is widely used in monitoring and security applications in various industries and has become a hot spot in the field of deep learning. Normality-representation-based methods perform well in certain scenarios but may ignore some aspects of the overall normality. Feature-extraction-based methods always take a process of pre-training, whose target differs from AD, leading to a decline in AD performance. In this paper, we propose a new AD method called deep Autoencoding One-Class (AOC), which learns features with AutoEncoder(AE). Meanwhile, the normal context vectors from AE are constrained into a hypersphere small enough, similar to one-class methods. With an objective function that optimizes the two assumptions simultaneously, AOC learns various aspects of normality, which is more effective for AD. Experiments on public datasets show that our method outperforms existing baseline approaches.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10095724