This paper concentrates on the entropy estimation of time series. Two new algorithms are introduced: Fast Approximate Entropy and Fast Sample Entropy. Their main advantage is their lower time complexity. Examples considered in the paper include interesting experiments with real-world data obtained from IT4Innovations' supercomputers Salomon and Anselm, as well as with data artificially created specifically to test the credibility of these new entropy analyzers.Web of Science7531454144
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse field...
. This chapter is concerned with two subjects. The first one is a method of signal preprocessing cal...
Time-series analyses are becoming ubiquitous, and they are used from Data Center monitoring to the s...
Approximate Entropy and especially Sample Entropy are recently frequently used algorithms for calcul...
Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regu...
Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regu...
: Sample entropy, an approximation of the Kolmogorov entropy, was proposed to characterize complexit...
Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regu...
Entropy metrics are valuable tools for use in systems science and engineering. Both of domains are ...
Partly motivated by entropy-estimation problems in neuroscience, we present a detailed and extensive...
Multiscale entropy (MSE) analysis was introduced in the 2002 to evaluate the complexity of a time se...
Multiscale entropy (MSE) analysis was introduced in the 2002 to evaluate the complexity of a time se...
Based on information theory, a number of entropy measures have been proposed since the 1990s to asse...
This paper describes the method which allows an estimation of information entropy in the meaning of ...
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse field...
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse field...
. This chapter is concerned with two subjects. The first one is a method of signal preprocessing cal...
Time-series analyses are becoming ubiquitous, and they are used from Data Center monitoring to the s...
Approximate Entropy and especially Sample Entropy are recently frequently used algorithms for calcul...
Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regu...
Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regu...
: Sample entropy, an approximation of the Kolmogorov entropy, was proposed to characterize complexit...
Sample Entropy is the most popular definition of entropy and is widely used as a measure of the regu...
Entropy metrics are valuable tools for use in systems science and engineering. Both of domains are ...
Partly motivated by entropy-estimation problems in neuroscience, we present a detailed and extensive...
Multiscale entropy (MSE) analysis was introduced in the 2002 to evaluate the complexity of a time se...
Multiscale entropy (MSE) analysis was introduced in the 2002 to evaluate the complexity of a time se...
Based on information theory, a number of entropy measures have been proposed since the 1990s to asse...
This paper describes the method which allows an estimation of information entropy in the meaning of ...
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse field...
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse field...
. This chapter is concerned with two subjects. The first one is a method of signal preprocessing cal...
Time-series analyses are becoming ubiquitous, and they are used from Data Center monitoring to the s...