Weakly Supervised Anomaly Detection for Streaming Data
Wei Zhang, Chris Challis · 2021
Anomaly detection for time-series data is an important component for monitoring tasks. Due to the massive and diverse types of streaming sources, it is impossible to manually label training data for large-scale applications. So existing solutions are based on unsupervised learning. However, each time-series data has its own domain specific property. Without incorporating human knowledge, unsupervised learning methods cannot match the performance of supervised learning. We introduce an anomaly detection solution which utilizes human knowledge and is suitable for large-scale applications. The proposed approach starts without any human labeling. Instead, human knowledge is obtained through feedbacks. We iteratively refine the training data to ensure that accurate models can be built. This makes it easily applicable to large-scale and heterogeneous tasks while enjoying the benefits of supervised learning.