Multi-feature unsupervised time series anomaly detection based on memory-augmented autoencoder - One-Class support vector machine

Unsupervised time series anomaly detection faces critical challenges when applied to high-dimensional and imbalanced data. Deep autoencoders tend to over-generalize, resulting in low reconstruction errors for abnormal samples and leading to missed detections. In turn, One-Class Support Vector Machines (OCSVM) rely on manually selected kernel functions, which often suffer from low computational efficiency. To address these issues, this paper proposes the Memory-augmented Autoencoder-One-Class Support Vector Machine (MemAE-OCSVM) model. It integrates a Memory-Augmented Autoencoder (MemAE) with OCSVM. The MemAE learns discriminative feature representations, replacing traditional kernel functions. This approach constructs an adaptive deep kernel function. Simultaneously, OCSVM establishes an optimal decision boundary in the feature space, enhancing anomaly identification capabilities. The model employs an end-to-end joint training framework. This enables synergistic optimization of feature learning and anomaly detection. The main innovations of this study include, introducing multi-feature fusion and a memory enhancement mechanism to improve the representation of complex normal patterns. Designing an adaptive deep kernel function based on MemAE, avoiding the limitations of manual kernel selection. Constructing an end-to-end unsupervised joint training framework to mitigate objective inconsistency issues common in multi-stage training. Experiments on three public datasets show that MemAE-OCSVM achieves average F1-score and recall values of 0. 934 and 0. 958, respectively. These results represent average improvements of 3. 8% in F1-score and 3. 2% in recall over the best baseline models. Ablation studies confirm the effectiveness of each module. Tests under varying anomaly rates demonstrate the model's strong robustness. This research provides an effective solution for real-time anomaly detection in complex scenarios. It offers both theoretical significance and practical application value.

Authors

• Guocheng Hao
• Hanxing Ruan
• Yuxin Li
• Qi Liu
• Cong Liu iFLYTEK CO.LTD., Hefei, China
• Zhekang Wang
• Xiangbo Li
• Jiantao Yu
• Juan Guo

Keywords

• Time series anomaly detection
• Multivariate time series
• Memory-augmented autoencoder (MemAE)
• Unsupervised learning
• Long Short-Term Memory (LSTM)
• One-class Support Vector Machines (OCSVM)