Iranian Journal of Fuzzy Systems، جلد ۸، شماره ۳، صفحات ۴۵-۶۶
عنوان فارسی AN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING
چکیده فارسی مقاله Improving time series forecasting
accuracy is an important yet often difficult task.
Both theoretical and empirical findings have
indicated that integration of several models is an effective
way to improve predictive performance, especially
when the models in combination are quite different. In this paper,
a model of the hybrid artificial neural networks and
fuzzy model is proposed for time series forecasting, using
autoregressive integrated moving average models. In the proposed
model, by first modeling the linear components, autoregressive integrated moving average models are
combined with the these hybrid models to yield a
more general and accurate forecasting model than the
traditional hybrid artificial neural networks and fuzzy models. Empirical results for  financial
time series forecasting indicate that the proposed model exhibits
effectively improved forecasting accuracy and hence is an appropriate forecasting tool for financial time
series forecasting.
کلیدواژه‌های فارسی مقاله Auto-regressive integrated moving average (ARIMA)، Artificial neural networks (ANNs)، Fuzzy regression، Fuzzy logic، Time series forecasting، Financial markets،
عنوان انگلیسی AN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING
چکیده انگلیسی مقاله Improving time series forecasting
accuracy is an important yet often difficult task.
Both theoretical and empirical findings have
indicated that integration of several models is an effective
way to improve predictive performance, especially
when the models in combination are quite different. In this paper,
a model of the hybrid artificial neural networks and
fuzzy model is proposed for time series forecasting, using
autoregressive integrated moving average models. In the proposed
model, by first modeling the linear components, autoregressive integrated moving average models are
combined with the these hybrid models to yield a
more general and accurate forecasting model than the
traditional hybrid artificial neural networks and fuzzy models. Empirical results for  financial
time series forecasting indicate that the proposed model exhibits
effectively improved forecasting accuracy and hence is an appropriate forecasting tool for financial time
series forecasting.
کلیدواژه‌های انگلیسی مقاله Auto-regressive integrated moving average (ARIMA), Artificial neural networks (ANNs), Fuzzy regression, Fuzzy logic, Time series forecasting, Financial markets
نویسندگان مقاله Mehdi Khashe |
Industrial Engineering Department, Isfahan University of Technol- ogy, Isfahan, Iran

Mehdi Bijari |
Industrial Engineering Department, Isfahan University of Technology, Isfahan, Iran

Seyed Reza Hejazi |
Industrial Engineering Department, Isfahan University of Tech- nology, Isfahan, Iran

نشانی اینترنتی http://ijfs.usb.ac.ir/article_286_8f60e4aa7f4205bae77bc7d15817e122.pdf
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