Investigating anthropically induced effects in streamflow dynamics by using permutation entropy and statistical complexity analysis: A case study

2016 ◽  
Vol 540 ◽  
pp. 1136-1145 ◽  
Author(s):  
Tatijana Stosic ◽  
Luciano Telesca ◽  
Diego Vicente de Souza Ferreira ◽  
Borko Stosic
Keyword(s):  
Complexity Analysis ◽  
Permutation Entropy ◽  
Statistical Complexity

scholarly journals Permutation Entropy and Statistical Complexity Analysis of Brazilian Agricultural Commodities

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1220 ◽  
Author(s):  
Fernando Henrique Antunes de Araujo ◽  
Lucian Bejan ◽  
Osvaldo A. Rosso ◽  
Tatijana Stosic
Keyword(s):  
Market Integration ◽  
Complexity Analysis ◽  
Commodity Prices ◽  
Food Prices ◽  
Permutation Entropy ◽  
Agricultural Commodities ◽  
Agricultural Commodity ◽  
Abrupt Increase ◽  
Statistical Complexity ◽  
Cattle Markets

Agricultural commodities are considered perhaps the most important commodities, as any abrupt increase in food prices has serious consequences on food security and welfare, especially in developing countries. In this work, we analyze predictability of Brazilian agricultural commodity prices during the period after 2007/2008 food crisis. We use information theory based method Complexity/Entropy causality plane (CECP) that was shown to be successful in the analysis of market efficiency and predictability. By estimating information quantifiers permutation entropy and statistical complexity, we associate to each commodity the position in CECP and compare their efficiency (lack of predictability) using the deviation from a random process. Coffee market shows highest efficiency (lowest predictability) while pork market shows lowest efficiency (highest predictability). By analyzing temporal evolution of commodities in the complexity–entropy causality plane, we observe that during the analyzed period (after 2007/2008 crisis) the efficiency of cotton, rice, and cattle markets increases, the soybeans market shows the decrease in efficiency until 2012, followed by the lower predictability and the increase of efficiency, while most commodities (8 out of total 12) exhibit relatively stable efficiency, indicating increased market integration in post-crisis period.

scholarly journals Concurrent Phenomena at the Reaction Path of the SN2 Reaction CH3Cl + F−. Information Planes and Statistical Complexity Analysis

Entropy ◽  
2013 ◽  
Vol 15 (12) ◽  
pp. 4084-4104 ◽  
Author(s):  
Moyocoyani Molina-Espíritu ◽  
Rodolfo Esquivel ◽  
Juan Angulo ◽  
Jesús Dehesa
Keyword(s):  
Complexity Analysis ◽  
Reaction Path ◽  
Statistical Complexity ◽  
Sn2 Reaction

Permutation Min-Entropy and Statistical Complexity Analysis of Electricity Spot Price

2015 ◽  
Vol 14 (04) ◽  
pp. 1550040 ◽  
Author(s):  
Qingju Fan ◽  
Dan Li
Keyword(s):  
Temporal Dynamics ◽  
Complexity Analysis ◽  
Spot Price ◽  
Temporal Correlations ◽  
Price Series ◽  
Statistical Complexity ◽  
Electricity Spot Price ◽  
Insight Into ◽  
Refined Classification

In this study, we investigate the subtle temporal dynamics of California 1999–2000 spot price series based on permutation min-entropy (PME) and complexity-entropy causality plane. The dynamical transitions of price series are captured and the temporal correlations of price series are also discriminated by the recently introduced PME. Moreover, utilizing the CECP, we provide a refined classification of the monthly price dynamics and obtain an insight into the stochastic nature of price series. The results uncover that the spot price signal presents diverse temporal correlations and exhibits a higher stochastic behavior during the periods of crisis.

scholarly journals Compacting the description of a time-dependent multivariable system and its multivariable driver by reducing the state vectors to aggregate scalars: the Earth's solar-wind-driven magnetosphere

2019 ◽  
Vol 26 (4) ◽  
pp. 429-443 ◽  
Author(s):  
Joseph E. Borovsky ◽  
Adnane Osmane
Keyword(s):  
Solar Wind ◽  
State Vector ◽  
Complexity Analysis ◽  
Low Noise ◽  
Time Dependent ◽  
Permutation Entropy ◽  
System Response ◽  
System A ◽  
Driven System ◽  
Time Dependent System

Abstract. Using the solar-wind-driven magnetosphere–ionosphere–thermosphere system, a methodology is developed to reduce a state-vector description of a time-dependent driven system to a composite scalar picture of the activity in the system. The technique uses canonical correlation analysis to reduce the time-dependent system and driver state vectors to time-dependent system and driver scalars, with the scalars describing the response in the system that is most-closely related to the driver. This reduced description has advantages: low noise, high prediction efficiency, linearity in the described system response to the driver, and compactness. The methodology identifies independent modes of reaction of a system to its driver. The analysis of the magnetospheric system is demonstrated. Using autocorrelation analysis, Jensen–Shannon complexity analysis, and permutation-entropy analysis the properties of the derived aggregate scalars are assessed and a new mode of reaction of the magnetosphere to the solar wind is found. This state-vector-reduction technique may be useful for other multivariable systems driven by multiple inputs.

Research on Attention EEG Based on the Multiscale Jensen-Shannon Divergence

2014 ◽  
Vol 529 ◽  
pp. 675-678
Author(s):  
Zheng Xia Zhang ◽  
Si Qiu Xu ◽  
Er Ning Zhou ◽  
Xiao Lin Huang ◽  
Jun Wang
Keyword(s):  
Time Series ◽  
Brain Function ◽  
Complexity Analysis ◽  
Function Parameter ◽  
Function Evaluation ◽  
Analysis Method ◽  
Eeg Signals ◽  
Eyes Closed ◽  
Statistical Complexity ◽  
Jensen Shannon Divergence

The article adopted the multiscale Jensen-Shannon Divergence analysis method for EEG complexity analysis. Then the study found that this method can distinguish between three different status (Eyes closed, count, in a daze) acquisition of EEG time series. It showed that three different states of EEG time series have significant differences. In each state of the three different states (Eyes closed, count, in a daze), we aimed at comparing and analyzing the statistical complexity of EEG time series itself and the statistical complexity of EEG time series shuffled data. It was found that there are large amounts of nonlinear time series in the EEG signals. This method is also fully proved that the multiscale JSD algorithm can be used to analyze attention EEG signals. The multiscale Jensen-Shannon Divergence statistical complexity can be used as a measure of brain function parameter, which can be applied to the auxiliary clinical brain function evaluation in the future.

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