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Open Access

ARTICLE

Robust Deep One-Class Classification Time Series Anomaly Detection

Zhengdao Yang1, Xuewei Wang2, Yuling Chen1,*, Hui Dou1, Haiwei Sang3
1 State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550000, China
2 College of Computer Science and Technology, Weifang University of Science and Technology, Weifang, 261000, China
3 School of Mathematics and Big Data, Guizhou Education University, Guiyang, 550018, China
* Corresponding Author: Yuling Chen. Email: email
(This article belongs to the Special Issue: Artificial Intelligence Algorithms and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.060564

Received 04 November 2024; Accepted 04 March 2025; Published online 10 April 2025

Abstract

Anomaly detection (AD) in time series data is widely applied across various industries for monitoring and security applications, emerging as a key research focus within the field of deep learning. While many methods based on different normality assumptions perform well in specific scenarios, they often neglected the overall normality issue. Some feature extraction methods incorporate pre-training processes but they may not be suitable for time series anomaly detection, leading to decreased performance. Additionally, real-world time series samples are rarely free from noise, making them susceptible to outliers, which further impacts detection accuracy. To address these challenges, we propose a novel anomaly detection method called Robust One-Class Classification Detection (ROC). This approach utilizes an autoencoder (AE) to learn features while constraining the context vectors from the AE within a sufficiently small hypersphere, akin to One-Class Classification (OC) methods. By simultaneously optimizing two hypothetical objective functions, ROC captures various aspects of normality. We categorize the input raw time series into clean and outlier sequences, reducing the impact of outliers on compressed feature representation. Experimental results on public datasets indicate that our approach outperforms existing baseline methods and substantially improves model robustness.

Keywords

Time series anomaly detection; self-supervised learning; robustness

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