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Recent advances in technology have brought major breakthroughs in datacollection, enabling a large amount of data to be gathered over time and thusgenerating time series. Mining this data has become an important task forresearchers and practitioners in the past few years, including the detection ofoutliers or anomalies that may represent errors or events of interest. Thisreview aims to provide a structured and comprehensive state-of-the-art onoutlier detection techniques in the context of time series. To this end, ataxonomy is presented based on the main aspects that characterize an outlierdetection technique.

A review on outlier/anomaly detection in time series data