Time series anomaly detection based on variational Transformer.

Saved in:

Bibliographic Details
Title:	Time series anomaly detection based on variational Transformer.
Authors:	XUE, Anrong¹, CHEN, Jie¹
Source:	Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. May2026, Vol. 48 Issue 5, p914-924. 11p.
Subjects:	Outlier detection, Transformer models, K-means clustering, Machine learning
Abstract:	Time series anomaly detection can identify anomalies in monitoring systems, allowing for timely measures to reduce failures and ensure system security. However, existing time series anomaly detection models struggle to effectively handle the nonlinear associations between time series data. To address this issue, a dual-branch learning model based on variational Transformer and Gaussian kernel is proposed, which constructs sequence associations and local associations separately. The difference metric between reconstruction errors and the two associations is used as the anomaly score, and the k-means algorithm is employed to automatically determine the anomaly threshold. Additionally, the position encoding in the Transformer is calibrated to reduce reconstruction errors. Comparative experimental results against nine baseline models on five public datasets indicate on various datasets, which the proposed model outperforms baseline models in most cases and is the only model that achieves an F1-score exceeding 90% across all five datasets, with an average F1-score on various datasets, which is 2.27 percentage points higher than that of the best baseline model. This indicates that the proposed model has significant advantages in correctness and can effectively improve the reliability and precision of time series anomaly detection. [ABSTRACT FROM AUTHOR]
	Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	Time series anomaly detection can identify anomalies in monitoring systems, allowing for timely measures to reduce failures and ensure system security. However, existing time series anomaly detection models struggle to effectively handle the nonlinear associations between time series data. To address this issue, a dual-branch learning model based on variational Transformer and Gaussian kernel is proposed, which constructs sequence associations and local associations separately. The difference metric between reconstruction errors and the two associations is used as the anomaly score, and the k-means algorithm is employed to automatically determine the anomaly threshold. Additionally, the position encoding in the Transformer is calibrated to reduce reconstruction errors. Comparative experimental results against nine baseline models on five public datasets indicate on various datasets, which the proposed model outperforms baseline models in most cases and is the only model that achieves an F1-score exceeding 90% across all five datasets, with an average F1-score on various datasets, which is 2.27 percentage points higher than that of the best baseline model. This indicates that the proposed model has significant advantages in correctness and can effectively improve the reliability and precision of time series anomaly detection. [ABSTRACT FROM AUTHOR]
ISSN:	1007130X
DOI:	10.3969/j.issn.1007-130X.2026.05.015