Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting
Автор: Mehdi Khashei, Mohammad Ali Montazeri, Mehdi Bijari
Статья в выпуске: 1, 2015 года.
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.
Artificial Neural Networks (ANNs), Time series forecasting, Auto-Regressive Integrated Moving Average (ARIMA), Combined forecast, Exchange Rate
Короткий адрес: https://readera.org/15010115
Список литературы Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting
- M. Khashei, "Soft Intelligent Decision Making (SIDM)", Ph.D. Thesis, Isfahan University of Technology (IUT), Industrial Engineering Department, 2013.
- M. CaZorzi, A. Kocięcki, M. Rubaszek, "Bayesian forecasting of real exchange rates with a Dornbusch prior", Economic Modelling, Vol. 46, Pages 53-60, 2015.
- T. Korol, "A fuzzy logic model for forecasting exchange rates", Knowledge-Based Systems, Vol. 67, Pages 49-60, 2014.
- O. Ince, "Forecasting exchange rates out-of-sample with panel methods and real-time data", Journal of International Money and Finance, Vol. 43, Pages 1-18, 2014.
- J.A. Batten, H. Kinateder, N. Wagner, "Multifractality and value-at-risk forecasting of exchange rates", Physica A: Statistical Mechanics and its Applications, Vol. 401, Pages 71-81, 2014.
- A. Garratt, E. Mise, "Forecasting exchange rates using panel model and model averaging", Economic Modelling, Vol. 37, Pages 32-40, 2014.
- M. Rout, B. Majhi, R. Majhi, G. Panda, "Forecasting of currency exchange rates using an adaptive ARMA model with differential evolution based training", Journal of King Saud University - Computer and Information Sciences, Vol. 26, Issue 1, Pages 7-18, 2014.
- C. Pierdzioch, J. Rülke, "On the directional accuracy of forecasts of emerging market exchange rates", International Review of Economics & Finance, Vol. 38, Pages 369-376, 2015.
- D. Ferraro, K. Rogoff, B. Rossi, "Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates", Journal of International Money and Finance, Vol. 54, Pages 116-141, 2015.
- L. Morales-Arias, G. V. Moura, "Adaptive forecasting of exchange rates with panel data", International Journal of Forecasting, Vol. 29, Issue 3, Pages 493-509, 2013.
- D. Zhou, "A New Hybrid Grey Neural Network Based on Grey Verhulst Model and BP Neural Network for Time Series Forecasting", International Journal of Mathematical Sciences and Computing, Vol. 5, No. 10, Pages 114-120, 2013.
- Khashei, M., Bijari, M., "Hybridization of the probabilistic neural networks with feed–forward neural networks for forecasting", Engineering Applications of Artificial Intelligence, Vol. 25, pp. 1277– 1288, 2012.
- M. Khashei, M. Bijari, "An artificial neural network (p, d, q) model for time series forecasting", Expert Systems with Applications, vol. 37, pp. 479– 489, 2010.
- M. Khashei, "Using general regression neural networks (GRNN) for forecasting", Behbod Journal, Isfahan University of Technology, 133, (2006), 28.
- Zhang, P, Min Qi b, G, "Neural network forecasting for seasonal and trend time series", European Journal of Operational Research 160, 501–514, 2005.
- M. Ture, I. Kurt, "Comparison of four different time series methods to forecast hepatitis A virus infection", Expert Systems with Applications 31 (2006) 41–46.
- J. W. Taylor, L. M. de Meneze, P. E. Mc Sharry, "A comparison of univariate methods for forecasting electricity demand up to a day ahead", International Journal of Forecasting 22 (2006) 1– 16.
- J. H. Kima, I. A. Moosab, "Forecasting international tourist flows to Australia: a comparison between the direct and indirect methods", Tourism Management 26 (2005) 69–78.
- V. Cho, "A comparison of three different approaches to tourist arrival forecasting", Tourism Management 24 (2003) 323–330.
- L. R. Weatherforda, S. E. Kimesb, "A comparison of forecasting methods for hotel revenue management", International Journal of Forecasting 19 (2003) 401–415.
- B. L. Smith, B. M. Williams, R. Keith, "Comparison of parametric and nonparametric models for traffic flow forecasting", Transportation Research Part C 10 (2002) 303–321.
- A. Sfetsos, "A comparison of various forecasting techniques applied to mean hourly wind speed time series", Renewable Energy 21 (2000) 23-35.
- I. Alon, R. J. Sadowski, "Forecasting aggregate retail sales: a comparison of artificial neural networks and traditional methods", Journal of Retailing and Consumer Services 8 (2001) 147-156.
- N. Meade, "A comparison of the accuracy of short term foreign exchange forecasting methods", International Journal of Forecasting 18, (2002) 67–83.
- M. T. Leunga, H. Daoukb," Forecasting stock indices: a comparison of classification and level estimation models", International Journal of Forecasting 16 (2000) 173–190.
- F. Lisi, R. A. Schiavo, "A comparison between neural networks and chaotic models for exchange rate prediction", Computational Statistics & Data Analysis 30, (1999) 87-102.
- Hsiao-Chien Tsui , "Exchange rate and pricing behavior: Comparison of Taiwan with Japan for manufacturing industries", Japan and the World Economy, Vol. 20, Pages 290-301, 2008.
- Amit Ghosh, "A comparison of exchange rate regime choice in emerging markets with advanced and low income nations for 1999–2011", International Review of Economics & Finance, Vol. 33, Pages 358-370, 2014.
- Kai-Li Wang, Christopher Fawson, Mei-Ling Chen, An-Chi Wu, "Characterizing information flows among spot, deliverable forward and non-deliverable forward exchange rate markets: A cross-country comparison", Pacific-Basin Finance Journal, Vol. 27, Pages 115-137, 2014.
- Seyed Amirhossein Razavi, Mary D. Still, Sharon J. White, Timothy G. Buchman, Michael J. Connor Jr.", Comparison of circuit patency and exchange rates between two different continuous renal replacement therapy machines", Journal of Critical Care, Vol. 29, Pages 272-277, 2014.
- P. Box, G.M. Jenkins, (1976)," Time Series Analysis: Forecasting and Control", Holden-day Inc, San Francisco, CA.
- M. Khashei, M. Bijari, GH A. Raissi, "Improvement of Auto-Regressive Integrated Moving Average Models Using Fuzzy Logic and Artificial Neural Networks (ANNs) ", Neurocomputing 72, pp. 956– 967, 2009.
- F. M. Tseng, G. H. Tzeng, H. C. Yu, B. J. C. Yuan, "Fuzzy ARIMA model for forecasting the foreign exchange market", Fuzzy Sets and Systems 118, (2001) 9-19.
- H Tanaka, "Fuzzy data analysis by possibility linear models", Fuzzy Sets and Systems 24(3), 363- 375, 1987.
- H. Ishibuchi, H. Tanaka, (1988), "Interval regression analysis based on mixed 0-1 integer programming problem", J. Japan Soc. Ind. Eng, 40 (5), 312-319.
- M. Khashei, F. Mokhatab Rafiei, M. Bijari, "Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH) Model for Forecasting the Foreign Exchange Markets", International Journal of Computational Intelligence Systems, Vol. 6, pp. 945-968, 2013.
- Khashei, M., Bijari, M., Raissi, G. A., "Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks", Computers & Industrial Engineering, Vol. 63, pp. 37– 45, 2012.
- Chen; A. S., Leung, M. T. Daouk, H.," Application of neural networks to an emerging financial market: forecasting and trading the Taiwan Stock Index", Computers & Operations Research 30, pp. 901–923, (2003).
- M. Khashei, M. Bijari, "Fuzzy artificial neural network (p, d, q) model for incomplete financial time series forecasting", Journal of Intelligent & Fuzzy Systems, Vol. 26, pp. 831–845, 2014.
- M. Khashei, S. R. Hejazi, M. Bijari, "A new hybrid artificial neural networks and fuzzy regression model for time series forecasting", Fuzzy Sets and Systems, Vol. 159, pp. 769– 786, 2008.
- M. Khashei, M. Bijari, "A novel hybridization of artificial neural networks and ARIMA models for time series forecasting", Applied Soft Computing, Vol. 11, pp. 2664– 2675, 2011.