ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation. (22nd October 2019)

• Record Type: Journal Article
• Authors: Ji, Cun; Zou, Xiunan; Liu, Shijun; Pan, Li
• Other Names: Wu Hao guestEditor.; Bie Rongfang guestEditor.; Pereira Charith guestEditor.; Rana Omer guestEditor.
• Is Part Of: Software, practice & experience. Volume 50:Number 11(2020)
• Page Start: 2065
• Page End: 2081
• Publication Date: 2019-10-22
• Subjects: anomaly detection -- biseries correlation -- time series -- relative outlier distance; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2756
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: The application of anomaly detection to data monitoring is a fundamental requirement of the public service systems of a smart city. Many detection methods have been proposed for identifying anomalous situations, including methods based on periodicity or biseries correlations. However, the detection results of these methods are not ideal. Thus, we present a new anomaly detection algorithm for time series based on the relative outlier distance (ROD) and biseries correlations. The proposed algorithm detects outliers based on the ROD and identifies abnormal points and change points based on biseries correlations. Experimental results show that our method achieves better recall and F1‐measure scores than various time series–based techniques while maintaining a high level of precision.