NDLI: Anomaly detection in time series data using a fuzzy c-means clustering

Content Provider	IEEE Xplore Digital Library
Author	Izakian, H. Pedrycz, W.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Alberta, Edmonton, AB, Canada (Izakian, H.; Pedrycz, W.)
Abstract	Detecting incident anomalies within temporal data - time series becomes useful in a variety of applications. In this paper, anomalies in time series are divided into two categories, namely amplitude anomalies and shape anomalies. A unified framework supporting the detection of both types of anomalies is introduced. A fuzzy clustering is employed to reveal the available structure within time series and a reconstruction criterion is used to assign an anomaly score to each subsequence. In the case of detecting anomalies in amplitude, the original representation of time series is used, while for detecting anomalies in shape an autocorrelation representation of time series to capture shape information is employed. Experimental studies concerning two real-world data sets are reported.
Sponsorship	IEEE
Starting Page	1513
Ending Page	1518
File Size	1323513
Page Count	6
File Format	PDF
ISBN	9781479903481
DOI	10.1109/IFSA-NAFIPS.2013.6608627
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Shape Euclidean distance Correlation Prototypes Electrocardiography Time measurement
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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