An anomaly detection framework for time series data: An interval-based approach

Abstract

Due to the high data volume and non-stationarity of time series data, it is very difficult to directly use the original data for anomaly detection. In this study, a novel framework of anomaly detection is proposed, whose intent is to capture more detailed data of time series’ shape and morphology characteristics by data representation to carry out anomaly detection. First, high-order differences and intervals are employed to realize data representation, and then such rectangles and cubes are constructed with the results of data representation for similarity measurement and anomaly detection. Compared with existing state-of-the-art methods, based on the experimental studies completed on large amount of datasets, the methods proposed in this framework are effective in detecting anomalies caused by changes in shape and amplitude. Meanwhile, it can detect anomalies with higher accuracy and better performance of data anomaly resolution.