Locality-Based Visual Outlier Detection Algorithm for Time Series
Joint Authors
Li, Zhihua
Li, Ziyuan
Yu, Ning
Wen, Steven
Source
Security and Communication Networks
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-22
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
Physiological theories indicate that the deepest impression for time series data with respect to the human visual system is its extreme value.
Based on this principle, by researching the strategies of extreme-point-based hierarchy segmentation, the hierarchy-segmentation-based data extraction method for time series, and the ideas of locality outlier, a novel outlier detection model and method for time series are proposed.
The presented algorithm intuitively labels an outlier factor to each subsequence in time series such that the visual outlier detection gets relatively direct.
The experimental results demonstrate the average advantage of the developed method over the compared methods and the efficient data reduction capability for time series, which indicates the promising performance of the proposed method and its practical application value.
American Psychological Association (APA)
Li, Zhihua& Li, Ziyuan& Yu, Ning& Wen, Steven. 2017. Locality-Based Visual Outlier Detection Algorithm for Time Series. Security and Communication Networks،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1202775
Modern Language Association (MLA)
Li, Zhihua…[et al.]. Locality-Based Visual Outlier Detection Algorithm for Time Series. Security and Communication Networks No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1202775
American Medical Association (AMA)
Li, Zhihua& Li, Ziyuan& Yu, Ning& Wen, Steven. Locality-Based Visual Outlier Detection Algorithm for Time Series. Security and Communication Networks. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1202775
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1202775