scholarly journals Generalized Multiscale Entropy Analysis: Application to Quantifying the Complex Volatility of Human Heartbeat Time Series

Entropy ◽  
2015 ◽  
Vol 17 (3) ◽  
pp. 1197-1203 ◽  
Author(s):  
Madalena Costa ◽  
Ary Goldberger
Keyword(s):  
Time Series ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Analysis Application

scholarly journals Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants

2021 ◽  
Author(s):  
Airton Monte Serrat Borin ◽  
Anne Humeau-Heurtier ◽  
Luiz Otavio Murta ◽  
Luiz Eduardo Virgilio Silva
Keyword(s):  
Time Series ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Short Term ◽  
Series Length ◽  
Short Time Series ◽  
Fuzzy Algorithms ◽  
Computing Performance ◽  
Similar Accuracy ◽  
Short Time

Abstract Multiscale entropy (MSE) analysis is a fundamental approach to access the complexity of a time series by estimating its information creation over a range of temporal scales. However, MSE may not be accurate or valid for short time series. This is why previous studies applied different kinds of algorithm derivations to short-term time series. However, no study has systematically analyzed and compared their reliabilities. This study compares the MSE algorithm variations adapted to short time series on both human and rat heart rate variability (HRV) time series. The most used variations of MSE are studied: composite MSE (CMSE), refined composite MSE (RCMSE), modified MSE (MMSE), and their fuzzy versions. We also analyze the errors in MSE estimations for a range of incorporated fuzzy exponents. The results show that fuzzy MSE versions-as a function of time series length-present minimal errors compared to the non-fuzzy algorithms. The traditional multiscale entropy algorithm with fuzzy counting (MFE) has similar accuracy to alternative algorithms with better computing performance. For the best accuracy, the findings suggest different fuzzy exponents according to the time series length.

scholarly journals SCALING BEHAVIOR AND COMPLEXITY OF THE PORTEVIN-LE CHATELIER EFFECT

2006 ◽  
Vol 20 (18) ◽  
pp. 1075-1092 ◽  
Author(s):  
A. SARKAR ◽  
P. BARAT
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Tensile Tests ◽  
Scaling Behavior ◽  
Multiscale Entropy ◽  
Dynamic Strain ◽  
Series Data ◽  
Finite Variance ◽  
Strain Rates ◽  
Entropy Analysis

The plastic deformation of dilute alloys is often accompanied by plastic instabilities due to dynamic strain aging and dislocation interaction. The repeated breakaway of dislocations from and their recapture by solute atoms leads to stress serrations and localized strain in the strain controlled tensile tests, known as the Portevin-Le Chatelier (PLC) effect. In this present work, we analyze the stress time series data of the observed PLC effect in the constant strain rate tensile tests on Al-2.5%Mg alloy for a wide range of strain rates at room temperature. The scaling behavior of the PLC effect was studied using two complementary scaling analysis methods: the finite variance scaling method and the diffusion entropy analysis. From these analyses we could establish that in the entire span of strain rates, PLC effect showed Levy walk property. Moreover, the multiscale entropy analysis is carried out on the stress time series data observed during the PLC effect to quantify the complexity of the distinct spatiotemporal dynamical regimes. It is shown that for the static type C band, the entropy is very low for all the scales compared to the hopping type B and the propagating type A bands. The results are interpreted considering the time and length scales relevant to the effect.

MULTISCALE ENTROPY ANALYSIS OF FINANCIAL TIME SERIES

2012 ◽  
Vol 11 (04) ◽  
pp. 1250033 ◽  
Author(s):  
JIANAN XIA ◽  
PENGJIAN SHANG
Keyword(s):  
Time Series ◽  
Financial Time Series ◽  
Sample Entropy ◽  
Scale Factor ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Financial Time

The paper mainly applies the multiscale entropy (MSE) to analyze the financial time series. The MSE is used to examine the complexity of a quantified system. Based on MSE, we propose multiscale cross-sample entropy (MSCE) to analyze the complexity and correlation of two time series. By comparing with the results, we find that both results present remarkable scaling characterization and the value of each log return of financial time series decreases with a increasing scale factor. From the results of MSE, we also find that the entropy of the Europe markets is lower than that of the Asia, but higher than that of the Americas. It means the MSE can distinguish different areas markets. The results of MSCE show that financial plate have high synchrony with the plate of Electron, IT and Realty. The MSCE can distinguish the highly synchronous plates.

MULTISCALE ENTROPY ANALYSIS OF TRAFFIC TIME SERIES

2013 ◽  
Vol 24 (02) ◽  
pp. 1350006 ◽  
Author(s):  
JING WANG ◽  
PENGJIAN SHANG ◽  
XIAOJUN ZHAO ◽  
JIANAN XIA
Keyword(s):  
Time Series ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Entropy Analysis ◽  
Traffic Time Series ◽  
Real World Datasets ◽  
Physiologic Time Series

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 when applied to real-world datasets. Then multi-scale entropy analysis (MSE) is introduced to solve this problem which has been widely used for physiologic time series. In this paper, we first apply the MSE method to different correlated series and obtain an interesting relationship between complexity and Hurst exponent. A modified MSE method called multiscale permutation entropy analysis (MSPE) is then introduced, which replaces the sample entropy (SampEn) with permutation entropy (PE) when measuring entropy for coarse-grained series. We employ the traditional MSE method and MSPE method to investigate complexities of different traffic series, and obtain that the complexity of weekend traffic time series differs from that of the workday time series, which helps to classify the series when making predictions.

Physiological Time Series Processing via Empirical Wavelet Transform

2020 ◽  
Vol 12 (5) ◽  
pp. 582-587
Author(s):  
Omkar Singh
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Multivariate Data ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Temporal Scales ◽  
Physiological Time ◽  
Mode Decomposition ◽  
Empirical Wavelet Transform

This paper presents the efficacy of empirical wavelet transform (EWT) for physiological time series processing. At first, EWT is applied to multivariate heterogeneous physiological time series. Secondly, EWT is used for the removal of fast temporal scales in multiscale entropy analysis. Empirical mode decomposition is an adaptive data analysis method in the sense that it does not require prior information about the signal statistics and tend to decompose a signal into various constituent modes. The utility of Standard EMD algorithm is however limited to single channel data as it suffers from the problems of mode alignment and mode mixing when applied channel wise for multivariate data. The standard EMD algorithm was extended to multivariate Empirical mode decomposition (MEMD) that can be used analyze a multivariate data. The MEMD can only be applied to multivariate data in which all the channels have equal data length. EWT is another adaptive technique for mode extraction in a signal using empirical scaling and wavelet functions. The multiscale entropy (MSE) algorithm is generally used to quantify the complexity of a time series. The original MSE approach utilizes a coarse-graining process for the removal of fast temporal scales in a time series which is equivalent to applying a finite impulse response (FIR) moving average filter. In Refined Multiscale entropy (RMSE), the FIR filter was replaced with a low pass Butterworth filter which exhibits a better frequency response than that of a FIR filter. In this paper we have presented a new approach for the removal of fast temporal scales based on empirical wavelet transform. The empirical wavelet transform is also used as an innovative filtering approach in multiscale entropy analysis.

scholarly journals Multiscale entropy analysis of electroseismic time series

2008 ◽  
Vol 8 (4) ◽  
pp. 855-860 ◽  
Author(s):  
L. Guzmán-Vargas ◽  
A. Ramírez-Rojas ◽  
F. Angulo-Brown
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Original Data ◽  
Sample Entropy ◽  
Entropy Method ◽  
Multiscale Entropy ◽  
Entropy Analysis ◽  
Complex Behaviour

Abstract. In this work we use the multiscale entropy method to analyse the variability of geo-electric time series monitored in two sites located in Mexico. In our analysis we consider a period of time from January 1995 to December 1995. We systematically calculate the sample entropy of electroseismic time series. Important differences in the entropy profile for several time scales are observed in records from the same station. In particular, a complex behaviour is observed in the vicinity of a M=7.4 EQ occurred on 14 September 1995. Besides, we also compare the changes in the entropy of the original data with their corresponding shuffled version.

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