@Article{JICS-7-121,
author = {Rahib H. Abiyev amdVasif Hidayat Abiyev},
title = {Differential Evaluation Learning of Fuzzy Wavelet Neural Networks for Stock Price Prediction},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {7},
number = {2},
pages = {121--130},
abstract = {Prediction of a stock price movement becomes very difficult problem in finance because of the 
presence  of  financial  instability  and  crisis.  The  time  series  describing  the  movement  of  stock  price  are 
complex  and  non  stationary.  This  paper  presents  the  development  of  fuzzy  wavelet  neural  networks  that 
combines the advantages of fuzzy systems and wavelet neural networks for prediction of stock prices. The 
structure of Fuzzy Wavelet Neural Networks (FWNN) is proposed and its learning algorithm is derived. The 
proposed network is constructed on the base of a set of TSK fuzzy rules that includes a wavelet function in 
the consequent part of each rules. The proposed FWNN structure is trained with differential evaluation (DE) 
algorithm.  The  use  of  DE  allows  quickly  train  the  FWNN  system  than  traditional  genetic  algorithm  (GA). 
FWNN  is  used  for  modelling  and  prediction  of  stock  prices.  Stock  prices  are  changed  every  day  and have 
high-order nonlinearity. The statistical data for the last three years are used for the development of FWNN 
prediction  model.  Effectiveness  of  the  proposed  system  is  evaluated  with  the  results  obtained  from  the 
simulation of FWNN based systems and with the comparative simulation results of other related models. 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22652.html}
}