无监督检测连续时间事件序列中的异常事件，精度优于现有方法。
Unsupervised Event Outlier Detection in Continuous Time
- 用生成对抗框架在线识别异常事件，生成器修正异常并辅助判别器学习。
- 在真实数据集上检测准确率显著高于当前最优无监督方法。
- 适合需要实时异常监测的金融、系统日志等场景。
事件序列数据记录了连续时间中事件的发生。基于时间点过程（TPPs）的事件序列预测已得到广泛研究，但无监督异常检测仍处于探索阶段。本文首次提出一种无监督异常检测方法，基于生成对抗网络（GAN）和强化学习的思想。我们训练一个‘生成器’来修正数据中的异常事件，同时训练一个‘判别器’区分修正后的数据与真实数据（可能包含异常）。关键洞察在于：若生成器错误修正，会生成与真实异常不同的新异常，从而作为数据增强供判别器学习。不同于传统基于GAN的异常检测方法，本方法利用生成器实现在线异常检测。实验结果表明，该方法在多个数据集上的异常检测准确率优于现有最先进方法。
原文摘要 · Abstract (English)
Event sequence data record the occurrences of events in continuous time. Event sequence forecasting based on temporal point processes (TPPs) has been extensively studied, but outlier or anomaly detection, especially without any supervision from humans, 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 train a 'generator' that corrects outliers in the data with a 'discriminator' that learns to discriminate the corrected data from the real data, which may contain outliers. A key insight is that if the generator made a mistake in the correction, it would generate anomalies that are different from the anomalies in the real data, so it serves as data augmentation for the discriminator learning. 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.
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