An Introduction to Outlier Analysis

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How researchers see this paper

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A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines (2018, background): “The distinction between anomalies and outliers is usually found in the literature, although according to Aggarwal [126] these terms, along with deviants , discordants or abnormalities , refer to the same concept.”
Deep Learning for Time Series Anomaly Detection: A Survey (2022, neutral): “The change in population in a particular region over several years is an example of nonlinear growth or decay depending on various dynamic factors.”
ADBench: Anomaly Detection Benchmark (2022, methodology): “§ A.1, recent book [3], and surveys [129, 150] for additional information.”
Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare (2022, methodology): “In contrast, isolated errors can be viewed as outliers and can be targeted using Shewhart control charts 31 , a popular technique in SPC, as well as general outlier detection methods 32 .”
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series (2019, methodology): “In the context of this paper, anomalies and outliers are used interchangeably as stated in [1].”
Deep Learning for Anomaly Detection: A Survey (2019, background): “Anomalies are also referred to as abnormalities, deviants, or outliers in the data mining and statistics literature (Aggarwal [2013]).”