Outlier Detection for Temporal Data: A Survey

• Published in: IEEE transactions on knowledge and data engineering 2014-09, Vol.26 (9), p.2250-2267
• Main Authors: Gupta, Manish, Jing Gao, Aggarwal, Charu C., Jiawei Han
• Format: Article
• Language: English
• Subjects: Computational modeling; Data mining; Distributed databases; Hidden Markov models; Mining methods and algorithms; Pattern matching; Predictive models; Software; Time series; Time series analysis
• ISSN: 1041-4347; 1558-2191
• DOI: 10.1109/TKDE.2013.184

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.