Evaluation of Short-Term Interval Models for Financial Time Series Forecasting

International Journal of Industrial Engineering

© 2015 by SSRG - IJIE Journal

Volume 2 Issue 3

Year of Publication : 2015

Authors : Kyung G.O and Dr. I.F.Myung-Suck

10.14445/23499362/IJIE-V2I5P102

How to Cite?

Kyung G.O and Dr. I.F.Myung-Suck, "Evaluation of Short-Term Interval Models for Financial Time Series Forecasting," SSRG International Journal of Industrial Engineering, vol. 2,  no. 3, pp. 5-8, 2015. Crossref, https://doi.org/10.14445/23499362/IJIE-V2I5P102

Abstract:

In current years, a variety of time series models have been planned for financial markets forecasting. In each case, the exactness of time series forecasting models are primary to make decision and hence the research for humanizing the efficiency of forecasting models have been curried on. Lots of researchers have compared diverse time series models mutually in order to establish more proficient once in financial markets. In this paper, the performance of four interval time series models including autoregressive integrated moving average (ARIMA), fuzzy autoregressive integrated moving average (FARIMA), hybrid ANNs and fuzzy (FANN) and Improved FARIMA models are compared together. Empirical results of exchange rate forecasting indicate that the FANN model is more acceptable than other those models. Consequently, it can be a suitable alternative model for interval forecasting of financial time series

Keywords:

Artificial Neural Networks (ANNs), Auto-Regressive Integrated Moving Average (ARIMA), Time series forecasting, Hybrid forecasts, Interval models, Exchange rate

References:

[1] Khashei, M., "Forecasting and Analysis Esfahan Steel Company Production Price in Tehran Metals Exchange with Artificial Neural Networks", Master of Science Thesis, Isfahan University of Technology, 2005. 
[2] Khashei, M., Bijari, M., "Foreign Exchange Rate Forecasting using a Hybrid Fuzzy and Auto Regressive Integrated Moving Average Model ", Esteghlal Journal, Volume 26, Issue 2, pp. 67- 75, 2008. 
[3] Ince, H., Trafalis, T., "A hybrid model for exchange rate prediction", Decision Support Systems, Volume 42, pp. 1045- 1062, 2006. 
[4] Chen, A., Leung, T., "Regression Neural Network for Error Correction in Foreign Exchange Forecasting and Trading", Computers & Operations Research, Volume 31, Issue 7, 2004, pp. 1049-1068. 
[5] Martens, M., “Forecasting Daily Exchange Rate Volatility using Intraday Returns”, Journal of International Money and Finance, Volume 20, Issue 1, 2001, pp. 1-23. 
[6] Khashei, M., Bijari, M., Raissi, G. A., "Improvement of Auto-Regressive Integrated Moving Average Models Using Fuzzy Logic and Artificial Neural Networks", Neurocomputing, Volume 72, 2009, pp. 956- 967. 
[7] Yu, L., Wang, S., Lai, K., “A Novel Nonlinear Ensemble Forecasting Model Incorporating GLAR and ANN for Foreign Exchange Rates”, Computers & Operations Research, Volume 32, Issue 10, 2005, pp. 2523- 2541. 
[8] Khashei, M., Bijari, M., "Using Fuzzy Auto-Regressive Integrated Moving Average (FARIMA) Model to Exchange Rate Forecasting", 6th Iranian Fuzzy Systems Conference, 2006. pp. 26-35, 
[9] Balaban, E., ”Comparative Forecasting Performance of Symmetric and Asymmetric Conditional Volatility Models of an Exchange Rate”, Economics Letters, Volume 83, Issue 1, 2004, pp 99-105. 
[10] Faust, J., Rogers, J., Wright, H., “Exchange rate forecasting: the errors we’ve really made", Journal of International Economics, Volume 60, Issue 1, 2003, pp 35- 59.