Forecasting non-stationary financial time series through genetic algorithm

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dc.creatorPorecha, M. B.
dc.creatorPanigrahi, P. K.
dc.creatorParikh, J. C.
dc.creatorKishtawal, C. M.
dc.creatorBasu, Sujit
dc.date2005-07-18
dc.date.accessioned2026-07-07T12:07:26Z
dc.date.available2026-07-07T12:07:26Z
dc.descriptionWe utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which the genetic algorithm's predictions are found to be quite accurate. The power law behavior in Fourier domain reveals an underlying self-affine dynamical behavior, well captured by the algorithm, in the form of an analytic equation. Remarkably, the same equation captures the trend of the Bombay stock exchange composite index quite well.
dc.description4 Pages and 5 figures
dc.identifierhttps://arxiv.org/abs/nlin/0507037
dc.identifierhttp://arxiv.org/abs/nlin/0507037
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/208972
dc.subjectChaotic Dynamics
dc.subjectData Analysis, Statistics and Probability
dc.subjectStatistical Finance
dc.titleForecasting non-stationary financial time series through genetic algorithm
dc.typetext

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