A Review on Outlier/Anomaly Detection in Time Series Data

Recent advances in technology have brought major breakthroughs in data collection, enabling a large amount of data to be gathered over time and thus generating time series. Mining this data has become an important task for researchers and practitioners in the past few years, including the detection of outliers or anomalies that may represent errors or events of interest. This review aims to provide a structured and comprehensive state-of-the-art on unsupervised outlier detection techniques in the context of time series. To this end, a taxonomy is presented based on the main aspects that characterize an outlier detection technique.

Automated summary

Recent technological advancements have enabled the collection of large amounts of data over time, leading to the generation of time series. Mining this data for outliers has become an important task for researchers and practitioners. This review aims to provide a structured and comprehensive overview of unsupervised outlier detection techniques in the context of time series, presenting a taxonomy based on key aspects characterizing outlier detection methods.