Table 7 Summary of the informational domain

The information is held in the degree of complexity, therefore: distance from periodicity and stochasticity, distance from a reference model, distance from a precedent pattern within the data

Approximate entropy

[4, 71, 77–79]

Conditional entropy

Bins

[25]

Compression entropy

[17, 81]

Fuzzy entropy

[77, 80]

Kullback-Leibler permutation entropy

Symbolic dynamics and phase space representation

[82]

Multiscale entropy

The complexity changes depending on the window length used in the analysis

[76, 83]

Predictive-based features

The data follows a model, and the deviation (prediction error) from that model describes changes in the system.

Multiple

[56, 84, 85]

Sample entropy

[4, 71, 77–79]

Shannon entropy

Bins

[25, 52, 53, 56, 86]

Similarity indexes

The comparison of the properties of two successive windows allows the detection of changes in a time series

Multiple

[26, 45, 87, 88]

Rényi entropy

Bins

[25]