TSFN: an Effective Time Series Anomaly Detection Approach via Transformer-based Self-feedback Network

As the scale of data on the Internet continues to increase, the management and monitoring of time series data are facing significant challenges. Efficient and stable time-series data anomaly detection methods are necessary for fields such as traffic detection, power grid operation and maintenance, financial stock market, and industry. However, there are fewer abnormal data labels in time series data, and the labeling cost is high. Traditional expert knowledge-based supervised methods have been difficult to adapt to large-scale data metric management and timely abnormal alarms. At the same time, the way based on the new neural network has an extensive time overhead when faced with massive data, and it isn’t easy to apply it in a real-time industrial environment. Therefore, we propose the TSFN model in this paper, an unsupervised method of a transformer-based self-feedback network. Which can capture timing dependencies, learn normal data distribution and improve the self-feedback ability for sensitive areas, and can be used to detect anomalies in multidimensional time series more quickly. Our experimental research on five public datasets shows that our method has fast training speed, good stability, excellent anomaly detection ability, and good generalization ability compared with the baseline method.