Figure A2.

Permutation entropy computed in sliding windows (a,b) and values of the CEofOP statistics (c,d) for artificial time series with sequences of ordinal patterns ${\pi }_1^{d,L}$ and ${\pi }_2^{d,L}$, respectively, where $d=3$, $L=240$. Both sequences of ordinal patterns have a change-point at $t=L/2$ (indicated by red vertical line) and are given by ${\pi }_1^{d,L}=(3,5,1\dots ,3,5,1,0,4,2,0,4,2,\dots ,0,4,2)$ and ${\pi }_2^{d,L}=(3,5,1\dots ,3,5,1,0,4,5,1,0,4,5,1,\dots ,0,4,5,1)$. Permutation entropy is computed in sliding windows of length $5(d+1)!=30$. While peaks of the CEofOP statistics clearly indicate the change-points, there is no straightforward way to detect changes from the values of permutation entropy.