论文标题
时间序列数据中关于异常/异常检测的综述
A review on outlier/anomaly detection in time series data
论文作者
论文摘要
技术的最新进展为数据收集带来了重大突破，从而可以随着时间的推移收集大量数据，从而产生了时间序列。在过去的几年中，挖掘这些数据已成为研究人员和从业人员的重要任务，包括检测可能代表错误或感兴趣事件的异常情况。这篇综述旨在在时间序列的情况下提供有关离群检测技术的结构化和全面的最新最新。为此，根据特征异常检测技术的主要方面提出了分类法。
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.
