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AN EXTENDED FUZZY ARTIFICIAL NEURAL NETWORKS MODEL FOR TIME SERIES FORECASTING | ||
Iranian Journal of Fuzzy Systems | ||
مقاله 5، دوره 8، شماره 3، دی 2011، صفحه 45-66 اصل مقاله (8.93 M) | ||
نوع مقاله: Research Paper | ||
شناسه دیجیتال (DOI): 10.22111/ijfs.2011.286 | ||
نویسندگان | ||
Mehdi Khashe* 1؛ Mehdi Bijari2؛ Seyed Reza Hejazi3 | ||
1Industrial Engineering Department, Isfahan University of Technol- ogy, Isfahan, Iran | ||
2Industrial Engineering Department, Isfahan University of Technology, Isfahan, Iran | ||
3Industrial Engineering Department, Isfahan University of Tech- nology, Isfahan, Iran | ||
چکیده | ||
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. | ||
کلیدواژهها | ||
Auto-regressive integrated moving average (ARIMA)؛ Artificial neural networks (ANNs)؛ Fuzzy regression؛ Fuzzy logic؛ Time series forecasting؛ Financial markets | ||
مراجع | ||
\bibitem{1} A. R. Arabpour and M. Tata, {\it Estimating the parameters of a fuzzy linear regression model}, Iranian Journal of Fuzzy Systems, {\bf5} (2008), 1-19. \bibitem{2} G. Armano, M. Marchesi and A. Murru, {\it A hybrid genetic-neural architecture for stock indexes forecasting}, Information Sciences, {\bf170} (2005), 3-33. \bibitem{1} J. M. Bates and W. J. Granger, {\it The combination of forecasts}, Operation Research, {\bf20} (1969), 451-468. \bibitem{1} P. Box and G. M. Jenkins, {\it Time series analysis: forecasting and control}, Holden-day Inc, San Francisco, CA, 1976. \bibitem{1} M. C. Brace, J. Schmidt and M. Hadlin, {\it Comparison of the forecasting accuracy of neural networks with other established techniques}, In: Proceedings of the First Forum on Application for weight elimination, IEEE Transactions on Neural Networks of Neural Networks to Power Systems, Seattle, WA (1991), 31-35. \bibitem{1} P. Chang, C. Liu and Y. Wang, {\it A hybrid model by clustering and evolving fuzzy rules for sales decision supports in printed circuit board industry}, Decision Support Systems, {\bf42} (2006), 1254-1269. \bibitem{1} S. M. Chen, {\it Forecasting enrollments based on fuzzy time series}, Fuzzy Sets and Systems, {\bf81(3)} (1996), 311--319, 1996. \bibitem{1} K. Y. Chen and C. H. Wang, {\it A hybrid SARIMA and support vector machines in forecasting the production values of the machinery industry in Taiwan}, Expert Systems with Applications, {\bf32} (2007), 254-264. \bibitem{1} S. M. Chen and N. Y. Chung, {\it Forecasting enrollments using high-order fuzzy time series and genetic algorithms}, International J. Intell. Syst., {\bf21} (2006), 485-501. \bibitem{1} R. Clemen, {\it Combining forecasts: a review and annotated bibliography with discussion}, International Journal of Forecasting, {\bf5} (1989), 559-608. \bibitem{1} J. W. Denton, {\it How good are neural networks for causal forecasting?}, The Journal of Business Forecasting, {\bf14(2)} (1995), 17-20. \bibitem{1} P. A. Fishwick, {\it Neural network models in simulation: a comparison with traditional modeling approaches}, In: Proceedings of Winter Simulation Conference, Washington, D. C., (1989), 702-710. \bibitem{1} W. R. Foster, F. Collopy and L. H. Ungar, {\it Neural network forecasting of short, noisy time series}, Computers and Chemical Engineering, {\bf16(4)} (1992), 293-297. \bibitem{1} I. Ginzburg and D. Horn, {\it Combined neural networks for time series analysis}, Neural Information Processing Systems, {\bf6} (1994), 224-231. \bibitem{1} T. H. Hann and E. Steurer, {\it Much ado about nothing? exchange rate forecasting: neural networks vs. linear models using monthly and weekly data}, Neurocomputing, {\bf10} (1996), 323-339. \bibitem{1} M. Haseyama and H. Kitajima, {\it An ARMA order selection method with fuzzy reasoning}, Signal Process, {\bf81} (2001), 1331-1335. \bibitem{1} H. Hassanpour, H. R. Maleki and M. A. Yaghoobi, {\it A note on evaluation of fuzzy linear regression models by comparing membership functions}, Iranian Journal of Fuzzy Systems, {\bf6} (2009), 1-6. \bibitem{1} M. Hibon and T. Evgeniou, {\it To combine or not to combine: selecting among forecasts and their combinations}, International Journal of Forecasting, {\bf21} (2005), 15-24. \bibitem{1} C. M. Hurvich and C. L. Tsai, {\it Regression and time series model selection in small samples}, Biometrica, {\bf76(2)} (1989), 297-307. \bibitem{1} H. B. Hwang, {\it Insights into neural-network forecasting time series corresponding to ARMA(p; q) structures}, Omega, {\bf29} (2001), 273-289. \bibitem{1} H. Ishibuchi and H. Tanaka, {\it Interval regression analysis based on mixed 0-1 integer programming problem}, J. Japan Soc. Ind. Eng, {\bf40(5)} (1988), 312-319. \bibitem{1} J. S. R. Jang, {\it ANFIS: adaptive-network-based fuzzy inference system}, IEEE Trans Syst, Man, Cybernet, {\bf23} (1993), 665-85. \bibitem{1} R. H. Jones, {\it Fitting autoregressions}, J. Amer. Statist. Assoc., {\bf70(351)} (1975), 590-592. \bibitem{1} M. Khashei, {\it Forecasting the Isfahan Steel Company production price in Tehran Metals Exchange using Artificial Neural Networks (ANNs)}, Master of Science Thesis, Isfahan University of Technology, 2005. \bibitem{1} M. Khashei, S. R. Hejazi and M. Bijari, {\it A new hybrid artificial neural networks and fuzzy regression model for time series forecasting}, Fuzzy Sets and Systems, {\bf159} (2008), 769-786. \bibitem{1} Y. Lin and W. G. Cobourn, {\it Fuzzy system models combined with nonlinear regression for daily ground-level ozone predictions}, Atmospheric Environment, {\bf41} (2007), 3502-3513. \bibitem{1} L. Ljung, {\it System Identification Theory for the User, Prentice-Hall}, Englewood Cliffs, NJ, 1987. \bibitem{1} J. T. Luxhoj, J. O. Riis and B. Stensballe, {\it A hybrid econometric-neural network modeling approach for sales forecasting}, Int. J. Prod. Econ., {\bf43} (1996), 175-192. \bibitem{1} S. Makridakis, A. Anderson, R. Carbone, R. Fildes. M. Hibdon, R. Lewandowski, J. Newton, E. Parzen and R. Winkler, {\it The accuracy of extrapolation (time series) methods: results of a forecasting competition}, Journal of Forecasting, {\bf1} (1982), 111-53. \bibitem{1} E. Mehdizadeh, S. Sadi-nezhad and R. Tavakkoli-moghaddam, {\it Optimization of fuzzy clustering criteria by a hybrid pso and fuzzy c-means clustering algorithm}, Iranian Journal of Fuzzy Systems, {\bf5} (2008), 1-14 \bibitem{1} T. Minerva and I. Poli, {\it Building ARMA models with genetic algorithms}, In: Lecture Notes in Computer Science, {\bf2037} (2001), 335-342. \bibitem{1} C. Ong, J. J. Huang and G. H. Tzeng, {\it Model identification of ARIMA family using genetic algorithms}, Appl. Math. Comput., {\bf164(3)} (2005), 885-912. \bibitem{1} P. F. Pai and C. S. Lin, {\it A hybrid ARIMA and support vector machines model in stock price forecasting}, Omega, {\bf33} (2005), 497-505. \bibitem{1} E. Pelikan, C. De Groot and D. Wurtz, {\it Power consumption in West-Bohemia: improved forecasts with decorrelating connectionist networks}, Neural Network, {\bf2} (1992), 701-712. \bibitem{1} M. J. Reid, {\it Combining three estimates of gross domestic product}, Economica, {\bf35} (1968), 431-444. \bibitem{1} R. Shibata, {\it Selection of the order of an autoregressive model by Akaike's information criterion}, Biometrika, {\bf AC-63(1)} (1976), 117-126. \bibitem{1} Z. Tang, C. Almeida and P. A. Fishwick, {\it Time series forecasting using neural networks us,} Box-Jenkins Methodology Simulation, {\bf57(5)} (1991), 303-310. \bibitem{1} Z. Tang and P. A. Fishwick, {\it Feedforward neural nets as models for time series forecasting}, ORSA Journal on Computing, {\bf5(4)} (1993), 374-385. \bibitem{1} T. Taskaya and M. C. Casey, {\it A comparative study of autoregressive neural network hybrids}, Neural Networks, {\bf18} (2005), 781-789. \bibitem{1} N. Terui and H. van Dijk, {\it Combined forecasts from linear and nonlinear time series models}, International Journal of Forecasting, {\bf18} (2002), 421-438. \bibitem{1} R. Tsaih, Y. Hsu and C. C. Lai, {\it Forecasting S$\&$P 500 stock index futures with a hybrid AI system}, Decision Support Systems, {\bf23} (1998), 161-174. \bibitem{1} F. M. Tseng, G. H. Tzeng, H. C. Yu and B. J. C. Yuan, {\it Fuzzy ARIMA model for forecasting the foreign exchange market}, Fuzzy Sets and Systems, {\bf118} (2001), 9-19. \bibitem{1} F. M. Tseng, H. C. Yu and G. H. Tzeng, {\it Combining neural network model with seasonal time series ARIMA model}, Technological Forecasting $\&$ Social Change, {\bf69} (2002), 71-87. \bibitem{1} M. V. D. Voort and M. Dougherty and S. Watson, {\it Combining Kohonen maps with ARIMA time series models to forecast traffic flow}, Transportation Research Part C: Emerging Technologies, {\bf4} (1996), 307-318. \bibitem{1} H. Wold, {\it A Study in the analysis of stationary time series}, Almgrist $\&$ Wiksell, Stockholm, 1938. \bibitem{1} H. K. Yu, {\it Weighted fuzzy time-series models for TAIEX forecasting}, Physica A, {\bf349} (2004), 609-624. \bibitem{1} L. Yu, S. Wang and K. K. Lai, {\it A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates}, Computers and Operations Research, {\bf32} (2005), 2523-2541. \bibitem{1} G. Yule, {\it Why do we sometimes get nonsense-correlations between time series? a study in sampling and the nature of time series}, J. R. Statist. Soc., {\bf89} (1926), 1-64. \bibitem{1} G. P. Zhang, {\it Time series forecasting using a hybrid ARIMA and neural network model}, Neurocomputing, {\bf50} (2003), 159-175. \bibitem{1} G. Zhang, B. E. Patuwo and M. Y. Hu, {\it Forecasting with artificial neural networks: the state of the art}, International Journal of Forecasting, {\bf14} (1998), 35-62. \bibitem{1} Z. J. Zhou and C. H. Hu, {\it An effective hybrid approach based on grey and ARMA for forecasting gyro drift, Chaos}, Solitons and Fractals, {\bf35} (2008), 525-529. | ||
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