[1F5-GS-10] AI application: anomaly detection 1

[1F5-GS-10-04] Time Series Anomaly Detection by Generative Adversarial Networks with Attention Mechanism

〇Tatsuki Kawamoto¹, Shogo Sakakura¹, Amar Zanashir², Kumiko Komatsu², Tomohiro Takagi¹ (1. Meiji University, 2. LAC Co., Ltd.)

Keywords:AI, Anomaly Detection, Machine Learning, Attention Mechanism, Generative Adversarial Networks

In recent years, more and more machine learning algorithms have been developed to detect anomalies in time series data. One of the approaches is the generative adversarial networks. Among these approaches, this study uses a method that learns from normal data and produces a high anomaly score when anomalous data is input. This method has advantages such as unsupervised learning and the ability to capture high-dimensional data distribution compared to conventional anomaly detection methods. On the other hand, the Attention mechanism has been widely used mainly in NLP. By using this attention mechanism, it is possible to capture the characteristics of the entire time series more directly than RNN. Although there is a possibility that the accuracy can be improved by applying this mechanism to the time series task, there are few studies that use the Attention mechanism in the research of anomaly detection by generative adversarial networks. In this study, we propose a new approach using adversarial networks with the Attention mechanism and show that our method improves the performance of time series anomaly detection compared with conventional methods.