Work place: Isfahan University of Technology, Isfahan, Iran
Research Interests: Soft Computing, Business Intelligence, Artificial Intelligence, Real-Time Computing
Mehdi Khashei studied industrial engineering at the Isfahan University of Technology (IUT) and received the Ph.D. degree in industrial engineering in 2005. He is author or co-author of about 100 scientific papers in international journals or communications to conferences with reviewing committee. His research interests include computational models of the brain, fuzzy logic, soft computing, nonlinear approximators, and time series forecasting.
DOI: https://doi.org/10.5815/ijmsc.2015.01.03, Pub. Date: 8 Jul. 2015
In today's world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. In the literature, Many different time series methods have been frequency compared together in order to choose the most efficient once. In this paper, the performances of four different interval ARIMA-base time series methods are evaluated in financial markets forecasting. These methods are including Auto-Regressive Integrated Moving Average (ARIMA), Fuzzy Auto-Regressive Integrated Moving Average (FARIMA), Fuzzy Artificial Neural Network (FANN) and Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH). Empirical results of exchange rate forecasting indicate that the fuzzy artificial neural network model is more satisfactory than other models.[...] Read more.
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