Threshold-free Anomaly Detection for Streaming Time Series through Deep Learning


Anomaly detection for streaming time series is a key issue in real applications, especially in the IT industry like ecommerce. Instead of employing the traditional threshold-based approach to achieve anomaly detection, we propose a threshold-free approach through deep learning in this paper. Two parallel pipelines: the intelligent baseline (a neural network assisted with several optimization steps) and the unsupervised detection (a combination of neural network and multiple machine learning algorithms) cooperatively and comprehensively analyze the streaming time series. The intelligent baseline performs well in cases where time series show clear periodic morphology, while the unsupervised detection excels at cases where efficiency is highly required and the periodicity is less clear. With this complementary design of the two parallel modules, the threshold-free anomaly detection can be achieved without the dependence on careful threshold design. Experiments prove that the proposed threshold-free approach obtains accurate predictions and reliable detections.

IEEE International Conference on Machine Learning and Applications (ICMLA)