DCDAD:Differentiated Context Dependency for Time Series Anomaly Detection Method

Time series anomaly detection aims to identify data points or segments in a time series that deviate from normal patterns.Enhancing detection accuracy by effectively utilizing contextual information in time series is a key role in constructing anomaly detection models.However,existing methods inadequately consider the differential context dependency in the data and lack explicit modeling of anomalous samples,resulting in poor discrimination between normal and anomalous samples and suboptimal detection performance.Therefore,this paper proposes a model that considers differential context dependency for time series anomaly detection(DCDAD),which enhances to learn the differential representations of context dependency.The DCDAD model captures temporal context dependency using self-attention mechanisms and learns hyperspheres for discriminating between normal and anomalous samples during the clustering process.By adopting the concept of anomaly injection,the dataset is augmented to address the issue of limited anomalous samples.Additionally,a targeted objective function for differentiated learning is designed to amplify the differences between normal and anomalous samples,thereby improving the anomaly detection performance.Extensive experiments conduct on five real-world time series datasets,and the results show an improvement of approximately 1.2% in terms of the <i>F</i>₁ score compared to state-of-the-art algorithms,validating the effectiveness of learning context dependency in a differentiated manner for improving the anomaly detection performance of the model.Furthermore,sensitivity analysis of parameters and ablation experiments validate the stability and effectiveness of the proposed model.