Аннотация
A method based on wavelet transform and genetic
programming is proposed for characterising and Modeling
variations at multiple scales in non-stationary time
series. The cyclic variations, extracted by wavelets
and smoothened by cubic splines, are well captured by
genetic programming in the form of dynamical equations.
For the purpose of illustration, we analyse two
different non-stationary financial time series, S&P CNX
Nifty closing index of the National Stock Exchange
(India) and Dow Jones industrial average closing values
through Haar, Daubechies-4 and continuous Morlet
wavelets for studying the character of fluctuations at
different scales, before modelling the cyclic behaviour
through GP. Cyclic variations emerge at intermediate
time scales and the corresponding dynamical equations
reveal characteristic behavior at different scales.
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