Unsupervised Anomaly Detection in Time Series Data via Enhanced VAE-Transformer Framework
Chunhao Zhang1,2, Bin Xie2,3,*, Zhibin Huo1
1 Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang, 050061, China
2 Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security, Shijiazhuang, 050024, China
3 College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, 050024, China
* Corresponding Author: Bin Xie. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.063151
Received 06 January 2025; Accepted 27 March 2025; Published online 11 April 2025
Abstract
Time series anomaly detection is crucial in finance, healthcare, and industrial monitoring. However, traditional methods often face challenges when handling time series data, such as limited feature extraction capability, poor temporal dependency handling, and suboptimal real-time performance, sometimes even neglecting the temporal relationships between data. To address these issues and improve anomaly detection performance by better capturing temporal dependencies, we propose an unsupervised time series anomaly detection method, VLT-Anomaly. First, we enhance the Variational Autoencoder (VAE) module by redesigning its network structure to better suit anomaly detection through data reconstruction. We introduce hyperparameters to control the weight of the Kullback-Leibler (KL) divergence term in the Evidence Lower Bound (ELBO), thereby improving the encoder module’s decoupling and expressive power in the latent space, which yields more effective latent representations of the data. Next, we incorporate transformer and Long Short-Term Memory (LSTM) modules to estimate the long-term dependencies of the latent representations, capturing both forward and backward temporal relationships and performing time series forecasting. Finally, we compute the reconstruction error by averaging the predicted results and decoder reconstruction and detect anomalies through grid search for optimal threshold values. Experimental results demonstrate that the proposed method performs superior anomaly detection on multiple public time series datasets, effectively extracting complex time-related features and enabling efficient computation and real-time anomaly detection. It improves detection accuracy and robustness while reducing false positives and false negatives.
Keywords
Anomaly detection; time series; autoencoder; transformer; unsupervised
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