FORECASTING SMOOTHED NON-STATIONARY TIME SERIES USING GENETIC ALGORITHMS

P. Norouzzadeh (), B. Rahmani () and M. S. Norouzzadeh ()
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P. Norouzzadeh: Department of Physics, University of Antwerp, Antwerp, Belgium;
B. Rahmani: Department of Mathematics, Sharif University of Technology, Tehran, Iran
M. S. Norouzzadeh: Department of Mathematical Sciences and Computer Engineering, Tarbiat Moallem University, Tehran, Iran

International Journal of Modern Physics C (IJMPC), 2007, vol. 18, issue 06, 1071-1086

Abstract: We introduce kernel smoothing method to extract the global trend of a time series and remove short time scales variations and fluctuations from it. A multifractal detrended fluctuation analysis (MF-DFA) shows that the multifractality nature of TEPIX returns time series is due to both fatness of the probability density function of returns and long range correlations between them. MF-DFA results help us to understand how genetic algorithm and kernel smoothing methods act. Then we utilize a recently developed genetic algorithm for carrying out successful forecasts of the trend in financial time series and deriving a functional form of Tehran price index (TEPIX) that best approximates the time variability of it. The final model is mainly dominated by a linear relationship with the most recent past value, while contributions from nonlinear terms to the total forecasting performance are rather small.

Keywords: Genetic algorithms; multifractality; time series; smoothing (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0129183107011133

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