Predicting financial time series data using hybrid model

Studies in Computational Intelligence

Volumn 650, Issue , 2016, Pages 19-41

  Al Hnaity, Bashar ^a   Abbod, Maysam ^a  

^a BRUNEL UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84991795024     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-33386-1_2     Document Type: Article

References (47)

1. Box, G.E.P., Draper, Norman R.: Empirical Model-building and Response Surfaces. Wiley, New York (1987)
2. Yudong, Z., Lenan W.: Stock market prediction of S&P 500 via combination of improved bco approach and bp neural network. Expert Syst. Appl. 36(5), pp. 805-818 (2009)
3. Asadi, S., Hadavandi, E., Mehmanpazir, F., Nakhostin, M.M.: Hybridization of evolutionary Levenberg-Marquardt neural networks and data pre-processing for stock market prediction. Knowl. Based Syst. 35, 245-258 (2012)
4. Hadavandi, E., Ghanbari, A., Abbasian-Naghneh, S.: Developing an evolutionary neural network model for stock index forecasting. In: Advanced Intelligent Computing Theories and Applications, pp. 407-415. Springer, Heidelberg (2010)
5. Hill, T., O’Connor, M., Remus, W.: Neural network models for time series forecasts. Manag. Sci. 42(7), 1082-1092 (1996)
6. Armano, G., Marchesi, M., Murru, A.:AHybrid Genetic-neural Architecture for Stock Indexes Forecasting. Inf. Sci. 170(1), 3-33 (2005)
7. Shen, W., Zhang, Y., Ma, X.: Stock return forecast with LS-SVM and particle swarm optimization. In: International Conference on Business Intelligence and Financial Engineering, 2009. BIFE’09, pp. 143-147. IEEE, New York (2009)
8. Kazem, A., Sharifi, E., Hussain, F.K., Saberi, M., Hussain, O.K.: Support vector regression with chaos-based firefly algorithm for stock market price forecasting. Appl. Soft Comput. 13(2), 947-958 (2013)
9. Pai, P.-F., Lin, K.-P., Lin, C.-S., Chang, P.-T.: Time series forecasting by a seasonal support vector regression model. Expert Syst. Appl. 37(6), 4261-4265 (2010)
10. Al-Hnaity, B., Abbod, M.: A novel hybrid ensemble model to predict FTSE100 index by combining neural network and EEMD. In: 2015 European Control Conference (ECC), pp. 3021-3028. IEEE, New York (2015)
11. Armstrong, J.S.: Combining forecasts: the end of the beginning or the beginning of the end? int. j. forecast. 5(4), 585-588 (1989)
12. Timmermann, A.: Forecast combinations. Handbook of Economic Forecasting, vol. 1, pp. 135-196 (2006)
13. Xiang, C., Fu, W.M.: Predicting the stock market using multiple models. In: 9th International Conference on Control, Automation, Robotics and Vision, 2006. ICARCV’06, pp. 1-6. IEEE, New York (2006)
14. Gooijer, D., Jan, G., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443-473 (2006)
15. LeBaron, B., Weigend, A.S.: A bootstrap evaluation of the effect of data splitting on financial time series. In: IEEE Transactions on Neural Networks, vol. 9(1), pp. 213-220. IEEE, New York (1998)
16. Balestrassi, P.P., Popova, E., de Paiva, A.P., Lima, J.W.M.: Design of experiments on neural network’s training for nonlinear time series forecasting. Neurocomputing 72(4), 1160-1178 (2009)
17. Diaz-Robles, L.A., Ortega, J.C., Fu, J.S., Reed, G.D., Chow, J.C., Watson, J.G., Moncada-Herrera, J.A.: A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile. Atmos. Environ. 42(35) 8331-8340 (2008)
18. Kubat, Miroslav: Neural Networks: A Comprehensive Foundation by Simon Haykin Macmillan. Cambridge University Press, Cambridge (1999)
19. Wang, J-J., Wang, J-Z., Zhang, Z-G., Guo, S.-P.: Stock index forecasting based on a hybrid model. Omega 40(6), 758-766 (2012)
20. Faruk, D.Ö.: A hybrid neural network and ARIMA model for water quality time series prediction. Eng. Appl. Artif. Intell. 23(4):586-594 (2010)
21. Cortes, C., Vapnik, V.: Support-vector Networks. In: Machine Learning, vol. 20(3), pp. 273-297. Springer, Heidelberg (1995)
22. Pontil, M., Verri, A.: Properties of support vector machines. In: Neural Computation, vol. 10(4), pp. 955-974. MIT Press, Cambridge (1998)
23. Osuna, E., Freund, R., Girosi, F.: Support Vector Machines: Training and Applications (1997)
24. Xia, Y., Liu, Y., Chen, Z.: Support Vector Regression for prediction of stock trend. In: 2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering (ICIII), vol. 2, pp. 123-126. IEEE, New York (2013)
25. Cherkassky, V., Ma, Y.: Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw. 17(1), 113-126 (2004)
26. Tay, F.E.H., Cao, L.: Application of support vector machines in financial time series forecasting. Omega, 29(4), 309-317 (2001)
27. Cao, L.-J., Tay, F.E.H.: Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans. Neural Netw. 14(6), 1506-1518 (2003)
28. Li, Y., Ma, W.: Applications of artificial neural networks in financial economics: a survey. In: 2010 International Symposium on Computational Intelligence and Design (ISCID), vol. 1, pp. 211-214. IEEE, New York (2010)
29. Theofilatos, K., Karathanasopoulos, A., Middleton, P., Georgopoulos, E., Likothanassis, S.: Modeling and trading FTSE100 index using a novel sliding window approach which combines adaptive differential evolution and support vector regression. In: Artificial Intelligence Applications and Innovations, pp. 486-496. Springer, Heidelberg (2013)
30. Hall, J.W.: Adaptive selection of US stocks with neural nets. In: Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets, pp. 45-65.Wiley, NewYork (1994)
31. Abu-Mostafa, Y.S., Atiya, A.F.: Introduction to financial forecasting. In: Applied Intelligence, vol. 6(3), pp. 205-213. Springer, Heidelberg (1996)
32. Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159-175 (2003)
33. Chatfield, C.: What is the bestmethod of forecasting. J. Appl. Stat. 15(1), 19-38 (1988)
34. Gupta, S., Wilton, P.C.: Combination of forecasts: an extension. Manag. Sci. 33(3), 356-372 (1987)
35. Christodoulos, C., Michalakelis, C., Varoutas, D.: Forecasting with limited data: combining ARIMA and diffusion models. Technol. Forecast. Soc. Change 77(4), 558-565 (2010)
36. John, H.H.:Adaptation inNatural andArtificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT press, Cambridge (1992)
37. Goldberg, D.E.: Genetic Algorithm in Search, Optimization and Machine Learning, vol. 1(98), p. 9. Addison Wesley Publishing Company, Reading (1989)
38. Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)
39. Wu, B., Chang, C.-L.: Using genetic algorithms to parameters (d, r) estimation for threshold autoregressive models. Comput. Stat. Data Anal. 38(3), 315-330 (2002)
40. Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques: Concepts and Techniques. Elsevier (2011)
41. Fuller, W.A.: Introduction to Statistical Time Series, vol. 428. Wiley, New York (2009)
42. Armstrong, J.S.: Principles of Forecasting: A Handbook for Researchers and Practitioners. Science and Business Media, vol. 30. Springer, Heidelberg (2001)
43. Hyndman, R.J., Koehler, A.B.: Another Look at Measures of Forecast Accuracy. Int. J. Forecast. 22(4), 679-688 (2006)
44. Wang, J., Wang, J.: Forecasting stock market indexes using principle component analysis and stochastic time effective neural network. Neurocomputing 156, 68-78 (2015)
45. Cao, Q., Leggio, K.B., Schniederjans, M.J.: A comparison between Fama and French’s model and artificial neural networks in predicting the Chinese stock market. Comput. Oper. Res. 32(10), 2499-2512 (2005)
46. Zhang, D., Zhou, L.: Discovering golden nuggets: data mining in financial application. In: IEEE Transactions on Systems, Man, and Cybernetics, Applications and Reviews, Part C, vol. 34(4), pp. 513-522. IEEE, New York (2004)
47. Hsu, C-W., Chang, C-C., Lin, C-J. et al.: A Practical Guide to Support Vector Classification (2003)