Investigating Semi-supervised Time Series Anomaly Detection Using AW-SNBAE Model - Guo, Mingyuan (International Journal of Computer Science and Information Technology, 2024)
In time series anomaly detection, most of the existing algorithms have difficulty in balancing the precision and dependence on labeled data. To solve this problem, a semi-supervised model called Adaptive Wavelet Sparse N-Beats AutoEncoder (AW-SNBAE) is proposed. This model has two main innovations. Firstly, an adaptive wavelet transform module is designed, which adaptively learns the wavelet function based on the global information of the time series, removes noise, and captures features of short-term and long-term changes. Secondly, the proposed Sparse N-Beats Auto Encoder (SNBAE) uses N-Beats as a base, and then, combines residual enhancement and sparse regularization strategies to construct the encoder and the decoder, respectively. Experimental results show that the AW-SNBAE model improves F1 scores by 8% in univariate time series anomaly detection and by 2% on average in multivariate detection. These results demonstrate that the model is able to exhibit excellent detection capability despite limited labeled data.