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