TY - JOUR
T1 - Outlier Detection for Temporal Data
T2 - A Survey
AU - Gupta, Manish
AU - Gao, Jing
AU - Aggarwal, Charu C.
AU - Han, Jiawei
N1 - Funding Information:
This work was supported in part by the U.S. Army Research Laboratory under Cooperative Agreement W911NF-11-2-0086 (Cyber-Security) and Cooperative Agreement W911NF-09-2-0053 (NS-CTA), in part by the U.S. Army Research Office under Cooperative Agreement W911NF-13-1-0193, and in part by U.S. National Science Foundation grant CNS-0931975, grant IIS-1017362, and grant IIS-1320617.
Publisher Copyright:
© 1989-2012 IEEE.
PY - 2014/9/1
Y1 - 2014/9/1
N2 - 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.
AB - 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.
KW - Data mining
KW - Mining methods and algorithms
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U2 - 10.1109/TKDE.2013.184
DO - 10.1109/TKDE.2013.184
M3 - Review article
AN - SCOPUS:84959505571
SN - 1041-4347
VL - 26
SP - 2250
EP - 2267
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 9
M1 - 6684530
ER -