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Example6.md

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Example 6: Multiscale Increment Entropy

Import a signal of uniformly distributed pseudorandom integers in the range [1 8] and create a multiscale entropy object with the following parameters: EnType = IncrEn(), embedding dimension = 3, a quantifying resolution = 6, normalization = true.

using EntropyHub # hide
X = ExampleData("randintegers");
Mobj = MSobject(IncrEn, m = 3, R = 6, Norm = true)

Calculate the multiscale increment entropy over 5 temporal scales using the modified graining procedure where:

y_j^{(\tau)} =\frac{1}{\tau } \sum_{i=\left(j-1\right)\tau +1}^{j\tau } x{_i}, 1<= j <= \frac{N}{\tau }

using EntropyHub # hide
X = ExampleData("randintegers"); # hide
Mobj = MSobject(IncrEn, m = 3, R = 6, Norm = true) # hide
MSx, _ = MSEn(X, Mobj, Scales = 5, Methodx = "modified");
MSx # hide

Change the graining method to return generalized multiscale increment entropy.

y_j^{(\tau)} =\frac{1}{\tau } \sum_{i=\left(j-1\right)\tau +1}^{j\tau } \left( x{_i} - \bar{x} \right)^{2}, 1<= j <= \frac{N}{\tau }

using EntropyHub # hide
X = ExampleData("randintegers"); # hide
Mobj = MSobject(IncrEn, m = 3, R = 6, Norm = true) # hide
MSx, _ = MSEn(X, Mobj, Scales = 5, Methodx = "generalized");
MSx # hide