A Method for Estimating the Entropy of Time Series Using Artificial Neural Networks
Measuring the predictability and complexity of time series using entropy is essential tool designing and controlling a nonlinear system. However, the existing methods have some drawbacks related to the strong dependence of entropy on the parameters of the methods. To overcome these difficulties, thi...
Guardado en:
Autores principales: | Andrei Velichko, Hanif Heidari |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
MDPI AG
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/6b26c1afc8c546a7992fae3357703527 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
On Conditional Tsallis Entropy
por: Andreia Teixeira, et al.
Publicado: (2021) -
Taming the Chaos in Neural Network Time Series Predictions
por: Sebastian Raubitzek, et al.
Publicado: (2021) -
Tight and Scalable Side-Channel Attack Evaluations through Asymptotically Optimal Massey-like Inequalities on Guessing Entropy
por: Andrei Tănăsescu, et al.
Publicado: (2021) -
Using Entropy to Evaluate the Impact of Monetary Policy Shocks on Financial Networks
por: Petre Caraiani, et al.
Publicado: (2021) -
Thermodynamic Consistency of the Cushman Method of Computing the Configurational Entropy of a Landscape Lattice
por: Samuel A. Cushman
Publicado: (2021)