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Security and Communication Networks
Volume 2017, Article ID 1869787, 10 pages
https://doi.org/10.1155/2017/1869787
Research Article

Locality-Based Visual Outlier Detection Algorithm for Time Series

1Department of Computer Science, School of Internet of Things Engineering, Jiangnan University, Jiangsu, Wuxi 214122, China
2Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA

Correspondence should be addressed to Zhihua Li; nc.ude.nangnaij@ilhz

Received 22 August 2016; Revised 8 June 2017; Accepted 6 July 2017; Published 22 August 2017

Academic Editor: Emanuele Maiorana

Copyright © 2017 Zhihua Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.