Name
Abstract | ||
|---|---|---|
Entropy is a powerful tool for the analysis of time series, as it allows describing the probability distributions of the possible state of a system, and therefore the information encoded in it. Nevertheless, important information may be codified also in the temporal dynamics, an aspect which is not usually taken into account. The idea of calculating entropy based on permutation patterns (that is, permutations defined by the order relations among values of a time series) has received a lot of attention in the last years, especially for the understanding of complex and chaotic systems. Permutation entropy directly accounts for the temporal information contained in the time series; furthermore, it has the quality of simplicity, robustness and very low computational cost. To celebrate the tenth anniversary of the original work, here we analyze the theoretical foundations of the permutation entropy, as well as the main recent applications to the analysis of economical markets and to the understanding of biomedical systems. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.3390/e14081553 | ENTROPY |
Keywords | Field | DocType |
permutation entropy,forbidden patterns,Shannon entropy,econophysics,EEG,epilepsy | Transfer entropy,Permutation,Rényi entropy,Information diagram,Joint entropy,Conditional entropy,Principle of maximum entropy,Statistics,Entropy (information theory),Mathematics | Journal |
Volume | Issue | Citations |
14 | 8 | 28 |
PageRank | References | Authors |
2.09 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Massimiliano Zanin | 1 | 514 | 32.55 |
| Luciano Zunino | 2 | 39 | 4.83 |
| Osvaldo A. Rosso | 3 | 58 | 13.07 |
| David Papo | 4 | 118 | 9.75 |