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ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation
Author(s) -
Ji Cun,
Zou Xiunan,
Liu Shijun,
Pan Li
Publication year - 2020
Publication title -
software: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.437
H-Index - 70
eISSN - 1097-024X
pISSN - 0038-0644
DOI - 10.1002/spe.2756
Subject(s) - anomaly detection , outlier , anomaly (physics) , series (stratigraphy) , computer science , precision and recall , measure (data warehouse) , algorithm , correlation , pattern recognition (psychology) , data mining , artificial intelligence , mathematics , geology , physics , paleontology , geometry , condensed matter physics
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.