Separation of Stochastic and Deterministic Information from Seismological Time Series with Nonlinear Dynamics and Maximum Entropy Methods

2007 ◽  
Author(s):  
Rafael M. Gutiérrez ◽  
Gina M. Useche ◽  
Elias Buitrago ◽  
Kevin H. Knuth ◽  
Ariel Caticha ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Maximum Entropy ◽  
Entropy Methods

scholarly journals Improving predictability of time series using maximum entropy methods

2015 ◽  
Vol 110 (1) ◽  
pp. 10003 ◽  
Author(s):  
G. Chliamovitch ◽  
A. Dupuis ◽  
A. Golub ◽  
B. Chopard
Keyword(s):  
Time Series ◽  
Maximum Entropy ◽  
Entropy Methods

Correlation in the heart rate data

1998 ◽  
Vol 2 ◽  
pp. 141-148
Author(s):  
J. Ulbikas ◽  
A. Čenys ◽  
D. Žemaitytė ◽  
G. Varoneckas
Keyword(s):  
Nonlinear Dynamics ◽  
Experimental Data ◽  
Heart Rate ◽  
Time Series ◽  
Cardiovascular Function ◽  
Statistical Properties ◽  
Rate Data ◽  
Heart Rate Data ◽  
Dynamical Nature ◽  
Analysis Of Time Series

Variety of methods of nonlinear dynamics have been used for possibility of an analysis of time series in experimental physiology. Dynamical nature of experimental data was checked using specific methods. Statistical properties of the heart rate have been investigated. Correlation between of cardiovascular function and statistical properties of both, heart rate and stroke volume, have been analyzed. Possibility to use a data from correlations in heart rate for monitoring of cardiovascular function was discussed.

Phenomenological Modelling

2018 ◽  
Author(s):  
Ray Huffaker ◽  
Marco Bittelli ◽  
Rodolfo Rosa
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Real World ◽  
Biological Diversity ◽  
False Positives ◽  
World Systems ◽  
Economic Activities ◽  
Essential Services ◽  
Low Dimension ◽  
Convergent Cross Mapping

Detecting causal interactions among climatic, environmental, and human forces in complex biophysical systems is essential for understanding how these systems function and how public policies can be devised that protect the flow of essential services to biological diversity, agriculture, and other core economic activities. Convergent Cross Mapping (CCM) detects causal networks in real-world systems diagnosed with deterministic, low-dimension, and nonlinear dynamics. If CCM detects correspondence between phase spaces reconstructed from observed time series variables, then the variables are determined to causally interact in the same dynamic system. CCM can give false positives by misconstruing synchronized variables as causally interactive. Extended (delayed) CCM screens for false positives among synchronized variables.

Characterization of Epileptic EEG Time Series (I): Gabor Transform and Nonlinear Dynamics Methods

1997 ◽  
pp. 179-226 ◽  
Author(s):  
Susana Blanco ◽  
Silvia Kochen ◽  
Rodrigo Quian Quiroga ◽  
Luis Riquelme ◽  
Osvaldo A. Rosso ◽  
...  
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Gabor Transform
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