Unsupervised Event Outlier Detection in Continuous Time

Event sequence data record the occurrence times of various events. Event sequence forecasting based on temporal point processes (TPPs) has been extensively studied, but outlier or anomaly detection, especially unsupervised detection of abnormal events, is still underexplored. In this work, we develop, to the best our knowledge, the first unsupervised outlier detection approach to detecting abnormal events. Our novel unsupervised outlier detection framework is based on ideas from generative adversarial networks (GANs) and reinforcement learning (RL). We try to train a “generator” that corrects outliers in the data with the help of a “discriminator” that learns to discriminate the corrected data from the real data, which may contain outliers. Different from typical GAN-based outlier detection approaches, our method employs the generator to detect outliers in an online manner. The experimental results show that our method can detect event outliers more accurately than the state-of-the-art approaches.