CiTranGAN: Channel-Independent Based-Anomaly Detection for Multivariate Time Series Data

Anomaly detection, as a critical task in time series data analysis, plays a pivotal role in ensuring industrial production safety, enhancing the precision of climate predictions and improving early warning for ocean disaster. However, due to the high dimensionality, redundancy, and non-stationarity inherent in time series data, rapidly and accurately identifying anomalies presents a significant challenge. This paper proposes a novel model CiTranGAN, which integrates the advantages of Transformer architecture, generative adversarial networks, and channel-independence strategies. In this model, the channel-independent strategy eliminates cross-channel interference and mitigates distribution drift in high-dimensional data. To mitigate redundancy and enhance multi-scale temporal feature representation, we constructed a feature extraction module that integrates downsampling, convolution, and interaction learning. To overcome the limitations of the traditional attention mechanism in detecting local trend variations, a hybrid dilated causal convolution-based multi-scale self-attention mechanism is proposed. Finally, experiments were conducted on five real-world multivariate time series datasets. Compared with the baseline models, CiTranGAN achieves average improvements of 12.48% in F1-score and 7.89% in AUC. In the ablation studies, CiTranGAN outperformed the channel-independent mechanism, the downsampling–convolution–interaction learning module, and the multi-scale convolutional self-attention mechanism, with respective average increases in AUC of 1.63%, 2.16%, and 3.47%, and corresponding average improvements in F1-score of 1.70%, 4.33%, and 2.04%, respectively. These experimental results demonstrate the rationality and effectiveness of the proposed model.