Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5011505 | Communications in Nonlinear Science and Numerical Simulation | 2017 | 13 Pages |
Abstract
We introduce the multi-dimensional ordinal arrays complexity as a generalized approximation of the ordinal Komogorov-Sinai entropy. The ordinal arrays entropy (OAE) is defined as the Shannon entropy of a series of m-ordinal patterns encoded symbols, while the ordinal arrays complexity (OAC) is defined as the differential of the OAE with respect to m. We theoretically establish that the OAC provides a better estimate of the complexity measure for short length time series. Simulations were carried out using discrete maps, and confirm the efficiency of the OAC as complexity measure from a small data set even in a noisy environment.
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Authors
J.S. Armand Eyebe Fouda, Wolfram Koepf, Sabir Jacquir,