Next Article in Journal / Special Issue
An Optimal Segmentation Method Using Jensen–Shannon Divergence via a Multi-Size Sliding Window Technique
Previous Article in Journal
Self-Organization during Friction of Slide Bearing Antifriction Materials
Previous Article in Special Issue
A Novel Method for PD Feature Extraction of Power Cable with Renyi Entropy
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(12), 7979-7995; doi:10.3390/e17127856

Wavelet-Tsallis Entropy Detection and Location of Mean Level-Shifts in Long-Memory fGn Signals

1
Department of Basic Sciences and Engineering (DCBeI), University of Caribe, Cancún 77528, Mexico
2
Instituto Tecnológico y de Estudios Superiores de Occidente (ITESO), Jesuit University of Guadalajara, San Pedro Tlaquepaque 45604, Mexico
3
Instituto Tecnológico de Sonora, Ciudad Obregón 85000, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
Received: 17 September 2015 / Revised: 20 November 2015 / Accepted: 24 November 2015 / Published: 4 December 2015
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory)
View Full-Text   |   Download PDF [1233 KB, uploaded 4 December 2015]   |  

Abstract

Long-memory processes, in particular fractional Gaussian noise processes, have been applied as models for many phenomena occurring in nature. Non-stationarities, such as trends, mean level-shifts, etc., impact the accuracy of long-memory parameter estimators, giving rise to biases and misinterpretations of the phenomena. In this article, a novel methodology for the detection and location of mean level-shifts in stationary long-memory fractional Gaussian noise (fGn) signals is proposed. It is based on a joint application of the wavelet-Tsallis q-entropy as a preprocessing technique and a peak detection methodology. Extensive simulation experiments in synthesized fGn signals with mean level-shifts confirm that the proposed methodology not only detects, but also locates level-shifts with high accuracy. A comparative study against standard techniques of level-shift detection and location shows that the technique based on wavelet-Tsallis q-entropy outperforms the one based on trees and the Bai and Perron procedure, as well. View Full-Text
Keywords: fractional Gaussian noise; long-memory; wavelet-Tsallis entropy; mean level-shifts; change point detection fractional Gaussian noise; long-memory; wavelet-Tsallis entropy; mean level-shifts; change point detection
Figures

Figure 1

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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Ramírez-Pacheco, J.C.; Rizo-Domínguez, L.; Cortez-González, J. Wavelet-Tsallis Entropy Detection and Location of Mean Level-Shifts in Long-Memory fGn Signals. Entropy 2015, 17, 7979-7995.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top