ADET: anomaly detection in time series with linear time

Abstract

Time series data is ubiquitous in financial, biomedical, and other areas. Anomaly detection in time series has been widely researched in these areas. However, most existing algorithms suffer from “curse of dimension” and may lose some information in the process of feature extraction. In this paper, we propose two new data structures named interval table (ITable) and extend interval table (EITable) for time series representation to capture more original information. We also proposed ADET: a novel Anomaly Detection algorithm based on EITable, which only needs linear time to detect meaningful anomalies. Extensive experiments on eleven data sets of UCR Repository, MIT-BIH datasets, and the BIDMC database show that ADET has overall good performance in terms of AUC-ROC and outperforms other algorithms in time complexity.