Journal of Engineering and Applied Sciences

Year: 2019
Volume: 14
Issue: 8
Page No. 2752 - 2763

Forecasting Stock Index Data Using Hybrid Models

Authors : Kumar Vasimalla, C. Narasimham and Deekshitha

References

Aladag, C.H., E. Egrioglu and C. Kadilar, 2009. Forecasting nonlinear time series with a hybrid methodology. Applied Mathe. Lett., 22: 1467-1470.
Direct Link  |  

Azadeh, A., M. Saberi and S.M. Asadzadeh, 2011. An adaptive network based fuzzy inference system-auto regression-analysis of variance algorithm for improvement of oil consumption estimation and policy making: The cases of Canada, United Kingdom and South Korea. Appl. Math. Modell., 35: 581-593.
CrossRef  |  Direct Link  |  

Chen, S.M., 1996. Forecasting enrollments based on fuzzy time series. Fuzzy Sets Syst., 81: 311-319.
CrossRef  |  

Chen, T.L., C.H. Cheng and H.J. Teoh, 2008. High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets. Physica A., 387: 876-888.
CrossRef  |  

Cheng, C.H., T.L. Chen and L.Y. Wei, 2010. A hybrid model based on rough sets theory and genetic algorithms for stock price forecasting. Inf. Sci., 180: 1610-1629.
CrossRef  |  Direct Link  |  

Connor, J., L.E. Atlas and D.R. Martin, 1991. Recurrent Networks and NARMA Modeling. Proceedings of the 4th International Conference on Neural Information Processing Systems (NIPS'91), December 2-5, 1991, Morgan Kaufmann Publishers, San Francisco, California, USA., ISBN:1-55860-222-4, pp: 301-308.

Engle, R.F., 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica J. Econom. Soc., 50: 987-1007.
CrossRef  |  Direct Link  |  

Ghore, S. and A. Goswami, 2015. Short term load forecasting of chhattisgarh grid using adaptive neuro fuzzy inference system. Intl. J. Sci. Res., 4: 841-846.
Direct Link  |  

Jilani, T.A. and S.M.A. Burney, 2008. A refined fuzzy time series model for stock market forecasting. Physica A, 387: 2857-2862.
CrossRef  |  

Jingtao, Y., C.L. Tan and H.L. Poh, 1999. Neural networks for technical analysis: A study on KLCI. Int. J. Theor. Applied Finance, 2: 221-241.

Krzysztof, M. and K. Halina, 2006. Correlation-based feature selection strategy in classification problems. Int. J. Applied Math. Comput. Sci., 16: 503-511.
Direct Link  |  

Laboissiere, L.A., R.A. Fernandes and G.G. Lage, 2015. Maximum and minimum stock price forecasting of Brazilian power distribution companies based on artificial neural networks. Appl. Soft Comput., 35: 66-74.
CrossRef  |  Direct Link  |  

Lee, W.J. and J. Hong, 2015. A hybrid dynamic and fuzzy time series model for mid-term power load forecasting. Intl. J. Electr. Power Energy Syst., 64: 1057-1062.
CrossRef  |  Direct Link  |  

Li, Y., W. Zhang, Q. Xiong, D. Luo and G. Mei et al., 2017. A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM. J. Mech. Sci. Technol., 31: 2711-2722.
CrossRef  |  Direct Link  |  

Majhi, B. and C.M. Anish, 2015. Multiobjective optimization based adaptive models with fuzzy decision making for stock market forecasting. Neurocomputing, 167: 502-511.
CrossRef  |  Direct Link  |  

Mohamad, M.A., D. Nasien, H. Hassan and H. Haron, 2015. A review on feature extraction and feature selection for handwritten character recognition. Intl. J. Adv. Comput. Sci. Appl., 6: 204-212.
CrossRef  |  Direct Link  |  

Ozkan, G. and M. Inal, 2014. Comparison of neural network application for fuzzy and ANFIS approaches for multi-criteria decision making problems. Appl. Soft Comput., 24: 232-238.
CrossRef  |  Direct Link  |  

Ravi, V., D. Pradeepkumar and K. Deb, 2017. Financial time series prediction using hybrids of chaos theory, multi-layer perceptron and multi-objective evolutionary algorithms. Swarm Evol. Comput., 36: 136-149.
CrossRef  |  Direct Link  |  

Roh, T.H., 2007. Forecasting the volatility of stock price index. J. Expert Syst. Appl., 33: 916-922.
CrossRef  |  Direct Link  |  

Su, C.H. and C.H. Cheng, 2016. A hybrid fuzzy time series model based on ANFIS and integrated nonlinear feature selection method for forecasting stock. Neurocomputing, 205: 264-273.
CrossRef  |  Direct Link  |  

Suganya, R. and R. Shanthi, 2012. Fuzzy C-means algorithm-a review. Intl. J. Sci. Res. Publ., 2: 1-3.
Direct Link  |  

Swasti, R. and S.P. Khuntia, 1994. Simulation Study for Automatic Generation Control of a Multi-Area Power System by ANFIS Approach. Prentice Hall, Englewood Cliffs, New Jersey, USA.,.

Wei, L.Y., 2016. A hybrid ANFIS model based on empirical mode decomposition for stock time series forecasting. Appl. Soft Comput., 42: 368-376.
CrossRef  |  Direct Link  |  

Yu, H.K., 2005. Weighted fuzzy time series models for TAIEX forecasting. Phys. A. Stat. Mech. Appl., 349: 609-624.
CrossRef  |  Direct Link  |  

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