Outdata = PE( indata, delay, order, windowSize ) Īx1 = subplot( 2, 1, 1 ) plot( indata, 'k', 'LineWidth', 0.2 ) WindowSize = 512 % 512 ordinal patterns in one sliding window Order = 3 % order 3 of ordinal patterns (4-points ordinal patterns) Indata( i ) = 4*indata( i - 1 )*( 1 - indata( i - 1 ) ) ĭelay = 1 % delay 1 between points in ordinal patterns (successive points) Indata = rand( 1, 7777 ) % generate random data pointsįor i = 4000:7000 % generate change of data complexity Fast permutation entropy ( MATLAB Central File Exchange. Efficiently measuring complexity on the basis of real-world data. The larger the values of permutation entropy (in the range from 0 to 1) are, the higher diversity of ordinal patterns is and the more complex input data are. outdata - (1 x (N - windowSize - order*delay) values of permutation entropy within since each sliding window contains windowSize ordinal patterns but uses in fact (windowSize + order*delay + 1) points). windowSize - size of sliding window ( = number of ordinal patterns within sliding window) order - order of the ordinal patterns (order + 1 is the number of points in ordinal patterns) delay - delay between points in ordinal patterns (delay = 1 means successive points) order = n-1 for n defined as in Ģ The values of permutation entropy are normalised by log((order+1)!) so that they are from as proposed in the original paper. See more ordinal-patterns based measures at NOTESġ Order of ordinal patterns is defined as in, i.e. Function outdata = PE( indata, delay, order, windowSize )Ĭomputes efficiently values of permutation entropy for orders=1.8 of ordinal patterns from 1D time series in sliding windows.
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