Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions

Zunino, Luciano; Olivares, F.; Scholkmann, Felix; Rosso, Osvaldo A.

doi:10.1016/j.physleta.2017.03.052

Cited by 119 publications

(91 citation statements)

References 77 publications

(92 reference statements)

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“…Another major problem is regarding the computation of permutation entropy, which omits repeated consecutive values. This deficiency was admitted by the authors in a later paper [11].…”

Section: Information Theory Featuresmentioning

confidence: 87%

Finger or Stylus: Their Impact on the Performance of On-line Signature Verification Systems

Antal

Bandi

2017

MACRo 2015

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Abstract:The widespread use of smartphones and the ability of these devices to digitize signatures have made it possible to sign electronic documents in this way. In this paper we compared two on-line signature databases in terms of verification performance: the MCYT containing signatures drawn by stylus pen, and MOBISIG containing finger drawn signatures. Performance evaluations were performed using both local and global systems. In the case of global systems, we evaluated the performance of a novel information theory features set. Little improvement was achieved by this feature set. There were large differences between the two databases in terms of performance. Finger drawn signatures collected by mobile device were proved inferior to signatures collected by digitizing tablet and its stylus.

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“…Another major problem is regarding the computation of permutation entropy, which omits repeated consecutive values. This deficiency was admitted by the authors in a later paper [11].…”

Section: Information Theory Featuresmentioning

confidence: 87%

Finger or Stylus: Their Impact on the Performance of On-line Signature Verification Systems

Antal

Bandi

2017

MACRo 2015

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“…Permutation entropy (PE) [33] is an algorithm based on time series phase space reconstruction and neighborhood value comparison, which can effectively describe the complexity of the system. PE has the advantages of simple algorithm, fast computing speed, and reliable calculation results [34]. MPE is an improved algorithm based on PE.…”

Section: Optimized Dmd Modes Via Mpementioning

confidence: 99%

“…If d is too large, the reconstruction of phase will homogenize the time series, which is not only time-consuming but also unable to reflect the small changes of the sequence. Bandt [33] and Zunino [34] suggested that d should be in the range 3-7. Time delay τ has little effect on the MPE of time series [35,36], and in most literatures [36][37][38], authors employ τ = 1.…”

Section: Optimized Dmd Modes Via Mpementioning

confidence: 99%

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Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Dang

et al. 2018

Entropy

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Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes. Then, multiscale permutation entropy calculation is performed to calculate the complexity of each DMD mode. Modes corresponding to the noise components are discarded by threshold technology, and we reconstruct the modes whose entropies are smaller than a threshold to recover the signal. By applying the algorithm to rolling bearing simulation signals and comparing with the result of wavelet transform, the effectiveness of the proposed method can be verified. Finally, the proposed method is applied to the experimental rolling bearing signals. Results demonstrated that the proposed approach has a good application prospect in noise reduction and fault feature extraction.

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“…If some ordinal patterns appear more frequently than others, the PE decreases, indicating that the signal is less random and more predictable [30]. For convenience, H p is typically normalized with log m!, namely,…”

Section: Permutation Entropymentioning

confidence: 99%

Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

2017

Entropy

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Abstract:The noise of near-infrared spectra and spectral information redundancy can affect the accuracy of calibration and prediction models in near-infrared analytical technology. To address this problem, the improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and permutation entropy (PE) were used to propose a new method for pretreatment and wavelength selection of near-infrared spectra signal. The near-infrared spectra of glucose solution was used as the research object, the improved CEEMDAN energy entropy was then used to reconstruct spectral data for removing noise, and the useful wavelengths are selected based on PE after spectra segmentation. Firstly, the intrinsic mode functions of original spectra are obtained by improved CEEMDAN algorithm. The useful signal modes and noisy signal modes were then identified by the energy entropy, and the reconstructed spectral signal is the sum of useful signal modes. Finally, the reconstructed spectra were segmented and the wavelengths with abundant glucose information were selected based on PE. To evaluate the performance of the proposed method, support vector regression and partial least square regression were used to build the calibration model using the wavelengths selected by the new method, mutual information, successive projection algorithm, principal component analysis, and full spectra data. The results of the model were evaluated by the correlation coefficient and root mean square error of prediction. The experimental results showed that the improved CEEMDAN energy entropy can effectively reconstruct near-infrared spectra signal and that the PE can effectively solve the wavelength selection. Therefore, the proposed method can improve the precision of spectral analysis and the stability of the model for near-infrared spectra analysis.

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Permutation entropy based time series analysis: Equalities in the input signal can lead to false conclusions

Abstract: The following work is licensed under a Creative Commons: Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) License.

Cited by 119 publications

References 77 publications

Finger or Stylus: Their Impact on the Performance of On-line Signature Verification Systems

Finger or Stylus: Their Impact on the Performance of On-line Signature Verification Systems

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Pretreatment and Wavelength Selection Method for Near-Infrared Spectra Signal Based on Improved CEEMDAN Energy Entropy and Permutation Entropy

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