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.

中文翻译：

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时间序列数据中的异常值/异常检测综述

最近的技术进步带来了数据收集方面的重大突破，可以随着时间的推移收集大量数据，从而生成时间序列。在过去几年中，挖掘这些数据已成为研究人员和从业人员的一项重要任务，包括检测可能代表错误或感兴趣事件的异常值或异常。本综述旨在为时间序列背景下的无监督异常值检测技术提供结构化和全面的最新技术。为此，基于异常值检测技术的主要特征提出了分类法。