Outlier Detection for Temporal Data: A Survey

Manish Gupta, Jing Gao, Charu C. Aggarwal, Jiawei Han

Research output: Contribution to journalReview article

Abstract

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.

Original languageEnglish (US)
Article number6684530
Pages (from-to)2250-2267
Number of pages18
JournalIEEE Transactions on Knowledge and Data Engineering
Volume26
Issue number9
DOIs
StatePublished - Sep 1 2014

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Keywords

  • Data mining
  • Mining methods and algorithms

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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