Most models for the time series of stock prices have centered on autoregresive (AR) processes. Traditionaly, fundamental Box-Jenkins analysis [3] have been the mainstream methodology used to develop time series models. Next, we briefly describe the develop a classical AR model for stock price forecasting. Then a fuzzy regression model is then introduced. Following this description, an artificial fuzzy neural network based on B-spline member ship function is presented as an alternative to the stock prediction method based on AR models. Finnaly, we present our preliminary results and some further experiments that we performed.
@article{urn:eudml:doc:39193,
title = {Stock price forecasting: Autoregressive modelling and fuzzy neural network.},
journal = {Mathware and Soft Computing},
volume = {7},
year = {2000},
pages = {139-148},
zbl = {0990.91025},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39193}
}
Marcek, Dusan. Stock price forecasting: Autoregressive modelling and fuzzy neural network.. Mathware and Soft Computing, Tome 7 (2000) pp. 139-148. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39193/