计算机科学
异常检测
离群值
时态数据库
数据挖掘
数据流挖掘
时间序列
软件
数据科学
多样性(控制论)
数据类型
人工智能
机器学习
程序设计语言
作者
Manish Gupta,Jing Gao,Charų C. Aggarwal,Jiawei Han
出处
期刊:IEEE Transactions on Knowledge and Data Engineering
[Institute of Electrical and Electronics Engineers]
日期:2014-09-01
卷期号:26 (9): 2250-2267
被引量:705
标识
DOI:10.1109/tkde.2013.184
摘要
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.
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