TITLE

Subspace Exponent Entropy and Its Application in Nonlinearity Test

AUTHOR(S)
YANG Zhao-fang; LIU Guang-yuan; CHENG Jing; WANG Lin-wei
PUB. DATE
January 2014
SOURCE
Journal of Signal Processing;Jan2014, Vol. 30 Issue 1, p86
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Detecting the nonlinearity of the time series is a prerequisite for time series analysis, while the selection of nonlinearity test statistics is crucial for the validity of the test results. We propose a new test statistic for nonlinearity test named subspace exponent entropy. Subspace exponent entropy divides the reconstructed state space of the time series into subspaces, and then measures the complexity of the phase point distribution in the subspaces. The nonlinearity test experiment tests the nonlinearity of five kinds of signals, including AR signal, Henon signal, Lorenz signal, ECG and SCR signal. The length of all the signals is 1000 points. In addition to subspace exponent entropy, we used other four test statistics commonly used in nonlinearity test named time reversibility, higher order autocovariance, nonlinear prediction error and approximate entropy. The experiment results show that the subspace exponent entropy can distinguish the nonlinearity of all signals, and has a high level of anti-noise performance. The subspace exponent entropy is an effective and stable test statistic for the nonlinearity test of short and noisy time series.
ACCESSION #
94755428

 

Related Articles

  • Minimax Estimation of the Parameter of Exponential Distribution based on Record Values. Lanping Li // International Journal of Information Technology & Computer Scien;Feb2014, Vol. 6 Issue 3, p47 

    Bayes estimators of the parameter of exponential distribution are obtained with non-informative quasi-prior distribution based on record values under three loss functions. These functions are weighted squared error loss, square log error loss and entropy loss functions. Finally the minimax...

  • Variance Entropy: A Method for Characterizing Perceptual Awareness of Visual Stimulus. Meng Hu; Hualou Liang // Applied Computational Intelligence & Soft Computing;2012, p1 

    Entropy, as a complexity measure, is a fundamental concept for time series analysis. Among many methods, sample entropy (SampEn) has emerged as a robust, powerful measure for quantifying complexity of time series due to its insensitivity to data length and its immunity to noise. Despite its...

  • Assessing the complexity of short-term heartbeat interval series by distribution entropy. Li, Peng; Liu, Chengyu; Li, Ke; Zheng, Dingchang; Liu, Changchun; Hou, Yinglong // Medical & Biological Engineering & Computing;Jan2015, Vol. 53 Issue 1, p77 

    Complexity of heartbeat interval series is typically measured by entropy. Recent studies have found that sample entropy (SampEn) or fuzzy entropy (FuzzyEn) quantifies essentially the randomness, which may not be uniformly identical to complexity. Additionally, these entropy measures are heavily...

  • Complexity-entropy analysis of daily stream flow time series in the continental United States. Serinaldi, Francesco; Zunino, Luciano; Rosso, Osvaldo // Stochastic Environmental Research & Risk Assessment;Oct2014, Vol. 28 Issue 7, p1685 

    Complexity-entropy causality plane (CECP) is a diagnostic diagram plotting normalized Shannon entropy $${\cal H}_S$$ versus Jensen-Shannon complexity $${\cal C}_{JS}$$ that has been introduced in nonlinear dynamics analysis to classify signals according to their degrees of randomness and...

  • MULTISCALE ENTROPY ANALYSIS OF TRAFFIC TIME SERIES. WANG, JING; SHANG, PENGJIAN; ZHAO, XIAOJUN; XIA, JIANAN // International Journal of Modern Physics C: Computational Physics;Feb2013, Vol. 24 Issue 2, p-1 

    There has been considerable interest in quantifying the complexity of different time series, such as physiologic time series, traffic time series. However, these traditional approaches fail to account for the multiple time scales inherent in time series, which have yielded contradictory findings...

  • Analytical properties of horizontal visibility graphs in the Feigenbaum scenario. Luque, Bartolo; Lacasa, Lucas; Ballesteros, Fernando J.; Robledo, Alberto // Chaos;Mar2012, Vol. 22 Issue 1, p013109 

    Time series are proficiently converted into graphs via the horizontal visibility (HV) algorithm, which prompts interest in its capability for capturing the nature of different classes of series in a network context. We have recently shown [B. Luque et al., PLoS ONE 6, 9 (2011)] that dynamical...

  • Entropy, complexity, and spatial information. Batty, Michael; Morphet, Robin; Masucci, Paolo; Stanilov, Kiril // Journal of Geographical Systems;Oct2014, Vol. 16 Issue 4, p363 

    We pose the central problem of defining a measure of complexity, specifically for spatial systems in general, city systems in particular. The measures we adopt are based on Shannon's (in Bell Syst Tech J 27:379-423, 623-656, ) definition of information. We introduce this measure and argue that...

  • Robust Exponential Synchronization for a Class of Master-Slave Distributed Parameter Systems with Spatially Variable Coefficients and Nonlinear Perturbation. Yang, Chengdong; Qiu, Jianlong; Yi, Kejia; Chen, Xiangyong; Zhang, Ancai; Chen, Xiao; Yang, Liuqing // Mathematical Problems in Engineering;6/15/2015, Vol. 2015, p1 

    This paper addresses the exponential synchronization problem of a class of master-slave distributed parameter systems (DPSs) with spatially variable coefficients and spatiotemporally variable nonlinear perturbation, modeled by a couple of semilinear parabolic partial differential equations...

  • Scaled Entropy for Dynamical Systems. Zhao, Yun; Pesin, Yakov // Journal of Statistical Physics;Jan2015, Vol. 158 Issue 2, p447 

    In order to characterize the complexity of a system with zero entropy we introduce the notions of scaled topological and metric entropies. We allow asymptotic rates of the general form $$e^{\alpha a(n)}$$ determined by an arbitrary monotonically increasing 'scaling' sequence $$a(n)$$ . This...

Share

Read the Article

Courtesy of THE LIBRARY OF VIRGINIA

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics