Author(s): Payam Norouzzadeh, Bahareh Rahmani, Mohammad Sadegh Norouzzadeh

Year: 2007

Pub. Info: International Journal of Modern Physics C

HTML tutorial


Generalized Hurst exponent, h(q) as a function of q for original, shuffled and surrogate of TEPIX returns and smoothed TEPIX returns time series obtained by MF-DFA2.




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.

BibTex: @article{norouzzadeh2007forecasting, title={Forecasting smoothed non-stationary time series using genetic algorithms}, author={Norouzzadeh, P and Rahmani, B and Norouzzadeh, MS}, journal={International Journal of Modern Physics C}, volume={18}, number={06}, pages={1071--1086}, year={2007}, publisher={World Scientific} }