Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition

Chen, Zhe; Li, Yaan; Liang, Hongtao; Yu, Jing

doi:10.1155/2019/1403829

Cited by 43 publications

(58 citation statements)

References 38 publications

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“…Of course, there are also different selections (for details, see the literature [25]). Some recent applications and development of permutation entropy also can be seen in related literature [26,27].…”

Section: Permutation Entropymentioning

confidence: 99%

Efficiency of Chinese Real Estate Market Based on Complexity-Entropy Binary Causal Plane Method

2020

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Real estate market is a complex system. A rational real estate market is not only helpful to people's living standards but also beneficial to countries’ macroeconomic stability. Is Chinese real estate market rational? This paper attempts to study the efficiency of Chinese real estate market by using the complexity-entropy binary causal plane method. We firstly discuss the formation mechanism of real estate price, which provides a theoretical basis for testing the efficiency, and compute the real estate market efficiency of 70 main Chinese cities. The results show that neither the whole market nor the main cities have reached the weak efficiency, and the efficiency and complexity of each city are different, and the relationship between them is significantly negative. In addition, this paper also compares the efficiency and complexity of Chinese real estate market with American real estate market. Then, some suggestions for the healthy development of Chinese real estate market in the future are put forward.

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Section: Permutation Entropymentioning

confidence: 99%

Efficiency of Chinese Real Estate Market Based on Complexity-Entropy Binary Causal Plane Method

2020

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“…x (1) x (2) x 3x (22) x (23) x (29) x (30) x(31) A time series {x(i) : 1 ≤ i ≤ 50} is given to illustrate the process for calculating SampEn(m, r, N). We specify m = 2 and r = 0.15 SD.…”

Section: Sample Entropymentioning

confidence: 99%

“…The dependence of the output value on the previous terms increases as the order p increases. Furthermore, as the order p increases, the correlation of the signal increases accordingly, making the model more predictable [23,29]. That is, the complexity of AR(p + 1) is lower than that of AR(p).…”

Section: Hierarchical Entropy Analysis For the Ar Processmentioning

confidence: 99%

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A Comparative Study of Multiscale Sample Entropy and Hierarchical Entropy and Its Application in Feature Extraction for Ship-Radiated Noise

Shen

2019

Entropy

Self Cite

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The presence of marine ambient noise makes it difficult to extract effective features from ship-radiated noise. Traditional feature extraction methods based on the Fourier transform or wavelets are limited in such a complex ocean environment. Recently, entropy-based methods have been proven to have many advantages compared with traditional methods. In this paper, we propose a novel feature extraction method for ship-radiated noise based on hierarchical entropy (HE). Compared with the traditional entropy, namely multiscale sample entropy (MSE), which only considers information carried in the lower frequency components, HE takes into account both lower and higher frequency components of signals. We illustrate the different properties of HE and MSE by testing them on simulation signals. The results show that HE has better performance than MSE, especially when the difference in signals is mainly focused on higher frequency components. Furthermore, experiments on real-world data of five types of ship-radiated noise are conducted. A probabilistic neural network is employed to evaluate the performance of the obtained features. Results show that HE has a higher classification accuracy for the five types of ship-radiated noise compared with MSE. This indicates that the HE-based feature extraction method could be used to identify ships in the field of underwater acoustic signal processing.

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“…Such features exhibit a very high discriminating power, and that is why they have been successfully employed in many applications [ 9 , 10 , 11 ]. However, there are ongoing efforts to further improve this discriminating power with new complexity estimation algorithms or by tweaking current ones [ 12 , 13 , 14 , 15 , 16 , 17 ], as is the case in this paper.…”

Section: Introductionmentioning

confidence: 99%

Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy

Cuesta–Frau

Picó

Vargas

et al. 2019

Entropy

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Many measures to quantify the nonlinear dynamics of a time series are based on estimating the probability of certain features from their relative frequencies. Once a normalised histogram of events is computed, a single result is usually derived. This process can be broadly viewed as a nonlinear I R n mapping into I R , where n is the number of bins in the histogram. However, this mapping might entail a loss of information that could be critical for time series classification purposes. In this respect, the present study assessed such impact using permutation entropy (PE) and a diverse set of time series. We first devised a method of generating synthetic sequences of ordinal patterns using hidden Markov models. This way, it was possible to control the histogram distribution and quantify its influence on classification results. Next, real body temperature records are also used to illustrate the same phenomenon. The experiments results confirmed the improved classification accuracy achieved using raw histogram data instead of the PE final values. Thus, this study can provide a very valuable guidance for the improvement of the discriminating capability not only of PE, but of many similar histogram-based measures.

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Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition

Cited by 43 publications

References 38 publications

Efficiency of Chinese Real Estate Market Based on Complexity-Entropy Binary Causal Plane Method

Efficiency of Chinese Real Estate Market Based on Complexity-Entropy Binary Causal Plane Method

A Comparative Study of Multiscale Sample Entropy and Hierarchical Entropy and Its Application in Feature Extraction for Ship-Radiated Noise

Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy

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