Anomaly detection for time series using temporal convolutional networks and Gaussian mixture model

Anomaly detection, as an important research field in the analysis of time series, has practical and significant applications in many occasions, such as network security, medical health, Internet of Things (IoT), fault diagnosis and so on. However, due to the inherent characteristics of time series, such as tremendous data volumes, the imbalance of normal data and abnormal data, additional constraints and challenges are added for anomaly detection for time series. We present a novel anomaly detection framework, which applies temporal convolutional networks to extract features of time series and combined Gaussian mixture model with Bayesian inference to detect anomalies of systems. In order to evaluate the effectiveness of our approach, experiments are carried out on two typical time series datasets including EEG dataset and current dataset of electrical equipment. The experiments indicate that temporal convolutional network can contribute to extracting salient features of time series and Gaussian mixture model with Bayesian inference has good generalization and reliability for anomaly detection. Meanwhile, the designed architecture and analysis approach of anomaly detection reveal the method's effectiveness and generalization in the feature extraction and anomaly detection for other time series.