  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
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.
