Outlier Detection for Temporal Data: A Survey

By Manish Gupta, Jing Gao, Charu C. Aggarwal, Jiawei Han. IEEE Transactions on Knowledge and Data Engineering, 2014

1,031 citations recorded by the source corpus.

In the statistics community, outlier detection for time series data has been studied for decades. Recently, with advances in hardware and software technology, there has been a large body of work on temporal outlier detection from a computational perspective within the computer science community. In particular, advances in hardware technology have enabled the availability of various forms of temporal data collection mechanisms, and advances in software technology have enabled a variety of data management mechanisms. This has fueled the growth of different kinds of data sets such as data streams, spatio-temporal data, distributed streams, temporal networks, and time series data, generated by a multitude of applications. There arises a need for an organized and detailed study of the work done in the area of outlier detection with respect to such temporal datasets. In this survey, we provide a comprehensive and structured overview of a large set of interesting outlier definitions for various forms of temporal data, novel techniques, and application scenarios in which specific definitions and techniques have been widely used.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/43d75d3a22db904d052d4c435e2d1f22be3887e0

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Citations in the corpus

Deep Learning for Anomaly Detection (2020, background): “eneities of anomalies. Also, it is challenging to detect anomalies from instances that may be dependent on each other such as by temporal, spatial, graph-based and other interdependency relationships [2,4,22,54]. •CH3: Data-efficient learning of normality/abnormality. Due to the difficulty and cost ”
Forecasting: theory and practice (2020, neutral): “tion of series (Gupta et al., 2013; Talagala et al., 2020b).”
A Unifying Review of Deep and Shallow Anomaly Detection (2020, background): “For time-series AD [169], [195], [201]–[204], where forecasting (i.”
Spatio-Temporal Data Mining (2017, neutral): “…time series [Bishop 1994; Keogh et al. 2005, 2002; Wei et al. 2005] as well as multivariate time series [Baragona and Battaglia 2007; Chen et al. 2008; Galeano et al. 2006; Takeishi and Yairi 2014] A survey of anomaly detection approaches for temporal data is presented in [Gupta et al. 2014].”
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks (2018, methodology): “Anomaly detection (a.k.a outlier detection) in dynamically changing networks is a long-standing problem deeply motivated in a number of application domains, such as social media, security, public health, and computational biology [1, 5, 7, 16, 33].”
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network (2020, background): “A comprehensive survey on the traditional techniques can be found in [6].”
Anomaly detection in dynamic networks: a survey (2015, background): “However, anomaly detection in dynamic networks 1 has been barely touched in existing works [11, 32].”
A Review on Outlier/Anomaly Detection in Time Series Data (2020, background): “Although a number of surveys on outlier detection methods have been presented in the literature [Aggarwal 2016; Aguinis et al. 2013; Chandola et al. 2007, 2009; Hodge and Austin 2004; Xu et al. 2019], very few focus on temporal data, including time series [Gupta et al. 2014b].”
Progress in Outlier Detection Techniques: A Survey (2019, background): “Detecting outliers in time series data [31], [57] and in detecting abnormal patterns in data streaming [10], [25], [72]–[74] is essential.”
Online Learning: A Comprehensive Survey (2018, background): “d to unsupervised and semi-supervised learning, and batch learning to online learning settings. More comprehensive surveys of classic anomaly detection studies can be found in (Chandola et al., 2009; Gupta et al., 2014). In this survey, we will focus online anomaly detection in unsupervised learning”