Differential Evaluation Learning of Fuzzy Wavelet Neural Networks for Stock Price Prediction
Cited by
Export citation
- BibTex
- RIS
- TXT
@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}
}
TY - JOUR
T1 - Differential Evaluation Learning of Fuzzy Wavelet Neural Networks for Stock Price Prediction
AU - Rahib H. Abiyev amdVasif Hidayat Abiyev
JO - Journal of Information and Computing Science
VL - 2
SP - 121
EP - 130
PY - 2024
DA - 2024/01
SN - 7
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22652.html
KW - Prediction of stock prise
KW - Fuzzy wavelet neural networks
KW - Differential Evolution
AB - 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.
Rahib H. Abiyev amdVasif Hidayat Abiyev. (2024). Differential Evaluation Learning of Fuzzy Wavelet Neural Networks for Stock Price Prediction.
Journal of Information and Computing Science. 7 (2).
121-130.
doi:
Copy to clipboard