Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting

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Mehdi Khashei 1,* Mohammad Ali Montazeri 2 Mehdi Bijari 1

1. Department of Industrial and Systems Engineering, Isfahan University of Technology, Isfahan, Iran

2. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran

* Corresponding author.


Received: 2 Apr. 2015 / Revised: 16 May 2015 / Accepted: 5 Jun. 2015 / Published: 8 Jul. 2015

Index Terms

Artificial Neural Networks (ANNs), Time series forecasting, Auto-Regressive Integrated Moving Average (ARIMA), Combined forecast, Exchange Rate


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.

Cite This Paper

Mehdi Khashei, Mohammad Ali Montazeri, Mehdi Bijari,"Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting", International Journal of Mathematical Sciences and Computing(IJMSC), Vol.1, No.1, pp.21-34, 2015.DOI: 10.5815/ijmsc.2015.01.03


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