Affiliations: College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
Correspondence:
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Corresponding author. Zean Tian, College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China. Tel.: +86 13974843579; Fax: +86 13974843579. E-mail: tianzean@126.com.
Abstract: Anomaly detection based on time series data is of great importance in many fields. Time series data produced by man-made systems usually include two parts: monitored and exogenous data, which respectively are the detected object and the control/feedback information. In this paper, a so-called G-CNN architecture that combined the gated recurrent units (GRU) with a convolutional neural network (CNN) is proposed, which respectively focus on the monitored and exogenous data. The most important is the introduction of a complementary double-referenced thresholding approach that processes prediction errors and calculates threshold, achieving balance between the minimization of false positives and the false negatives. The outstanding performance and extensive applicability of our model is demonstrated by experiments on two public datasets from aerospace and a new server machine dataset from an Internet company. It is also found that the monitored data is close associated with the exogenous data if any, and the interpretability of the G-CNN is discussed by visualizing the intermediate output of neural networks.
Keywords: Anomaly detection, CNN, GRU, time series, deep learning
