Forecasting smoothed non-stationary time series using genetic algorithms

Norouzzadeh, P ; Sharif University of Technology | 2007

139 Viewed
  1. Type of Document: Article
  2. DOI: 10.1142/S0129183107011133
  3. Publisher: 2007
  4. Abstract:
  5. 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. © World Scientific Publishing Company
  6. Keywords:
  7. Genetic algorithms ; Multifractality ; Time series ; Smoothing
  8. Source: International Journal of Modern Physics C ; Volume 18, Issue 6 , 2007 , Pages 1071-1086 ; 01291831 (ISSN)
  9. URL: https://www.worldscientific.com/doi/abs/10.1142/S0129183107011133