scholarly journals Noise-driven topological changes in chaotic dynamics

2021 ◽  
Vol 31 (10) ◽  
pp. 103115
Author(s):  
Gisela D. Charó ◽  
Mickaël D. Chekroun ◽  
Denisse Sciamarella ◽  
Michael Ghil
Keyword(s):  
Chaotic Dynamics ◽  
Topological Changes

Chaotic Dynamics

1996 ◽  
Author(s):  
Gregory L. Baker ◽  
Jerry P. Gollub
Keyword(s):  
Chaotic Dynamics

Forecasting and Estimating SETAR Models under the Influence of Chaotic Dynamics

2012 ◽  
Author(s):  
Ricardo Gimeno ◽  
Ruth Mateos de Cabo ◽  
Lorenzo Escot ◽  
Pilar Grau ◽  
Elena Olmedo
Keyword(s):  
Chaotic Dynamics

Experimental evidences for chaotic dynamics in the vocalizations of the humpback whale megaptera novaeangliae

2006 ◽  
Vol 1 (2) ◽  
pp. 180-188
Author(s):  
A. Facchini ◽  
F. Delogu ◽  
L. Lambroni ◽  
F.M. Pulselli ◽  
E.B.P. Tiezzi
Keyword(s):  
Chaotic Dynamics ◽  
Humpback Whale ◽  
Megaptera Novaeangliae

Nonlinear Dynamics and Chaos: Application to Financial Markets in Pakistan

1994 ◽  
Vol 33 (4II) ◽  
pp. 1417-1429 ◽  
Author(s):  
Nasir M. Khiui
Keyword(s):  
Financial Markets ◽  
Chaotic Dynamics ◽  
Ad Hoc ◽  
Linear Models ◽  
Brain Research ◽  
Economic Data ◽  
Differential Equation Models ◽  
Short Run ◽  
The Business Cycle ◽  
Random Shocks

Recently there has been an increased interest in the theory of chaos by macroeconomists and fmancial economists. Originating in the natural sciences, applications of the theory have spread through various fields including brain research, optics, metereology, and economics. The attractiveness of chaotic dynamics is its ability to generate large movements which appear to be random, with greater frequency than linear models. Two of the most striking features of any macro-economic data are its random-like appearance and its seemingly cyclical character. Cycles in economic data have often been noticed, from short-run business cycles, to 50 years Kodratiev waves. There have been many attempts to explain them, e.g. Lucas (1975), who argues that random shocks combined with various lags can give rise to phenomena which have the appearance of cycles, and Samuelson (1939) who uses the familiar multiplier accelerator model. The advantage of using non-linear difference (or differential) equation models to explain the business cycle is that it does not have to rely on ad hoc unexplained exogenous random shocks.

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