EAD: An Efficient Anomaly Detection Algorithm for Multivariate Time Series

Dehong Ma, Bo Ding, Dawei Feng, Hui Liu

Abstract: Anomaly detection based on deep learning has been widely used in IT infrastructure management. Driven by large amount of data, deep learning (DL) based algorithms can achieve higher accuracy compared to rule-based algorithms. However, the computational complexity of these algorithms is much higher than traditional rule-based ones, which will cost lots of time and computing resources. This limits the application of such algorithms in some actual systems. Therefore, it is necessary to improve the detection efficiency (e.g., execution time, resource occupation, etc.) of existing DL-based algorithms to make them more practical. In this work, we propose an efficient detection approach to solve this problem by combining DL-based algorithms and rule-based algorithms. Specificly, we apply rule-based algorithms to filter the original data roughly and then exploit the DL-based algorithms to make the final decision. We evaluate our approach using two state-of-the-art deep learning anomaly detection approaches with three real-world datasets. The results show that the proposed approach can significantly improve detection efficiency, saving about 80%~95% of execution time under the premise that the accuracy is nearly unchanged.