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How to cite this paper
APA: Do, Q & Trang, T. (2020). Forecasting Vietnamese stock index: A comparison of hierarchical ANFIS and LSTM. Decision Science Letters, 9(2), 193-206.
Chicago/Turabian: Do, Q & Trang, T. 2020. "Forecasting Vietnamese stock index: A comparison of hierarchical ANFIS and LSTM." Decision Science Letters 9, no. 2 (2020): 193-206.
AMA: Do, Q & Trang, T. Forecasting Vietnamese stock index: A comparison of hierarchical ANFIS and LSTM. Decision Science Letters. 2020;9(2):193-206.
References
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Bao, T. Q., & My, B. T. T. (2019). Forecasting stock index based on hybrid artificial neural network models. Science & Technology Development Journal-Economics-Law and Management, 3(1), 52–57.
Ake, B. (2010). The role of stock market development in economic growth: evidence from some Euronext countries. International Journal of Financial Research, 1(1), 14-20.
Boyacioglu, M. A., & Avci, D. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Systems with Applications, 37(12), 7908-7912.
Brown, M., Bossley, K. M., Mills, D. J., & Harris, C. J. (1995, March). High dimensional neurofuzzy systems: overcoming the curse of dimensionality. In Proceedings of 1995 IEEE International Conference on Fuzzy Systems. (Vol. 4, pp. 2139-2146). IEEE.
Buragohain, M., & Mahanta, C. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8(1), 609-625.
Esfahanipour, A., & Mardani, P. (2011, June). An ANFIS model for stock price prediction: The case of Tehran stock exchange. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (pp. 44-49). IEEE.
Fakhrahmad, S. M., Rezapour, A. R., Jahromi, M. Z., & Sadreddini, M. H. (2012). A new fuzzy rule-based classification system for word sense disambiguation. Intelligent Data Analysis, 16(4), 633-648.
GüNeri, A. F., Ertay, T., & YüCel, A. (2011). An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Systems with Applications, 38(12), 14907-14917.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. In Prentice Hall.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
Jeenanunta, C., Chaysiri, R., & Thong, L. (2018). Stock Price Prediction With Long Short-Term Memory Recurrent Neural Network. 2018 International Conference on Embedded Systems and Intelligent Technology & International Conference on Information and Communication Technology for Embedded Systems (ICESIT-ICICTES), 1–7. IEEE.
Jian, Z., & Song, L. (2016). Financial Time Series Analysis Model for Stock Index Forecasting. International Journal of Simulation--Systems, Science & Technology, 17(16).
Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319.
Kennedy, E. P., Condon, M., & Dowling, J. (2003). Torque-ripple minimisation in switched reluctance motors using a neuro-fuzzy control strategy. Proceedings of the IASTED International Conference on Modelling and Simulation.
Kyungjoo, L., Sehwan, Y., & John, J. J. (2007). Neural network model vs. SARIMA model in forecasting Korean stock price index. Issues in Information Systems, 8(2), 372–378.
Liu, C., Wang, J., Xiao, D., & Liang, Q. (2016). Forecasting s&p 500 stock index using statistical learning models. Open Journal of Statistics, 6(06), 1067.
Ertunc, H. M., & Hosoz, M. (2008). Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system. International Journal of Refrigeration, 31(8), 1426-1436.
Nauck, D., Klawonn, F., & Kruse, R. (1997). Foundations of neuro-fuzzy systems. John Wiley & Sons, Inc.
Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS one, 11(5), e0155133.
Rahman, M. M., & Salahuddin, M. (2009). The determinants of economic growth in Pakistan: does stock market development play a major role? Proceedings of the 38th Australian Conference of Economists (ACE 2009), 1–22. Economic Society of Australia (South Australian Branch).
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
Senol, D., & Ozturan, M. (2008). Stock price direction prediction using artificial neural network approach: The case of Turkey. Journal of Artificial Intelligence, 1(2), 70-77.
Singh, T. N., Kanchan, R., Verma, A. K., & Saigal, K. (2005). A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass. Journal of Earth System Science, 114(1), 75-86.
Sugeno, M. (1985). An introductory survey of fuzzy control. Information Sciences, 36(1-2), 59-83.
Takagi, H., & Hayashi, I. (1991). NN-driven fuzzy reasoning. International Journal of Approximate Reasoning, 5(3), 191-212.
Touny, M. A. (2012). Stock Market Development and Economic Growth: Empirical Evidence from Some Arab Countries. Arab Journal of Administration, 32(1).
Wei, M., Bai, B., Sung, A. H., Liu, Q., Wang, J., & Cather, M. E. (2007). Predicting injection profiles using ANFIS. Information Sciences, 177(20), 4445-4461.
Yusof, N., Ahmad, N. B., Othman, M. S., & Mohammad, F. A. (2012). A concise fuzzy rule base to reason student performance based on rough-fuzzy approach. In Fuzzy inference system–theory and applications (pp. 63-82). INTECH Open Access Publisher.
Azadeh, A., Saberi, M., Anvari, M., Azaron, A., & Mohammadi, M. (2011). An adaptive network based fuzzy inference system–genetic algorithm clustering ensemble algorithm for performance assessment and improvement of conventional power plants. Expert Systems with Applications, 38(3), 2224-2234.
Bao, T. Q., & My, B. T. T. (2019). Forecasting stock index based on hybrid artificial neural network models. Science & Technology Development Journal-Economics-Law and Management, 3(1), 52–57.
Ake, B. (2010). The role of stock market development in economic growth: evidence from some Euronext countries. International Journal of Financial Research, 1(1), 14-20.
Boyacioglu, M. A., & Avci, D. (2010). An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: the case of the Istanbul stock exchange. Expert Systems with Applications, 37(12), 7908-7912.
Brown, M., Bossley, K. M., Mills, D. J., & Harris, C. J. (1995, March). High dimensional neurofuzzy systems: overcoming the curse of dimensionality. In Proceedings of 1995 IEEE International Conference on Fuzzy Systems. (Vol. 4, pp. 2139-2146). IEEE.
Buragohain, M., & Mahanta, C. (2008). A novel approach for ANFIS modelling based on full factorial design. Applied Soft Computing, 8(1), 609-625.
Esfahanipour, A., & Mardani, P. (2011, June). An ANFIS model for stock price prediction: The case of Tehran stock exchange. In 2011 International Symposium on Innovations in Intelligent Systems and Applications (pp. 44-49). IEEE.
Fakhrahmad, S. M., Rezapour, A. R., Jahromi, M. Z., & Sadreddini, M. H. (2012). A new fuzzy rule-based classification system for word sense disambiguation. Intelligent Data Analysis, 16(4), 633-648.
GüNeri, A. F., Ertay, T., & YüCel, A. (2011). An approach based on ANFIS input selection and modeling for supplier selection problem. Expert Systems with Applications, 38(12), 14907-14917.
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780.
Jang, J.-S. R., Sun, C.-T., & Mizutani, E. (1997). Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. In Prentice Hall.
Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3), 665-685.
Jeenanunta, C., Chaysiri, R., & Thong, L. (2018). Stock Price Prediction With Long Short-Term Memory Recurrent Neural Network. 2018 International Conference on Embedded Systems and Intelligent Technology & International Conference on Information and Communication Technology for Embedded Systems (ICESIT-ICICTES), 1–7. IEEE.
Jian, Z., & Song, L. (2016). Financial Time Series Analysis Model for Stock Index Forecasting. International Journal of Simulation--Systems, Science & Technology, 17(16).
Kara, Y., Boyacioglu, M. A., & Baykan, Ö. K. (2011). Predicting direction of stock price index movement using artificial neural networks and support vector machines: The sample of the Istanbul Stock Exchange. Expert systems with Applications, 38(5), 5311-5319.
Kennedy, E. P., Condon, M., & Dowling, J. (2003). Torque-ripple minimisation in switched reluctance motors using a neuro-fuzzy control strategy. Proceedings of the IASTED International Conference on Modelling and Simulation.
Kyungjoo, L., Sehwan, Y., & John, J. J. (2007). Neural network model vs. SARIMA model in forecasting Korean stock price index. Issues in Information Systems, 8(2), 372–378.
Liu, C., Wang, J., Xiao, D., & Liang, Q. (2016). Forecasting s&p 500 stock index using statistical learning models. Open Journal of Statistics, 6(06), 1067.
Ertunc, H. M., & Hosoz, M. (2008). Comparative analysis of an evaporative condenser using artificial neural network and adaptive neuro-fuzzy inference system. International Journal of Refrigeration, 31(8), 1426-1436.
Nauck, D., Klawonn, F., & Kruse, R. (1997). Foundations of neuro-fuzzy systems. John Wiley & Sons, Inc.
Qiu, M., & Song, Y. (2016). Predicting the direction of stock market index movement using an optimized artificial neural network model. PloS one, 11(5), e0155133.
Rahman, M. M., & Salahuddin, M. (2009). The determinants of economic growth in Pakistan: does stock market development play a major role? Proceedings of the 38th Australian Conference of Economists (ACE 2009), 1–22. Economic Society of Australia (South Australian Branch).
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
Senol, D., & Ozturan, M. (2008). Stock price direction prediction using artificial neural network approach: The case of Turkey. Journal of Artificial Intelligence, 1(2), 70-77.
Singh, T. N., Kanchan, R., Verma, A. K., & Saigal, K. (2005). A comparative study of ANN and neuro-fuzzy for the prediction of dynamic constant of rockmass. Journal of Earth System Science, 114(1), 75-86.
Sugeno, M. (1985). An introductory survey of fuzzy control. Information Sciences, 36(1-2), 59-83.
Takagi, H., & Hayashi, I. (1991). NN-driven fuzzy reasoning. International Journal of Approximate Reasoning, 5(3), 191-212.
Touny, M. A. (2012). Stock Market Development and Economic Growth: Empirical Evidence from Some Arab Countries. Arab Journal of Administration, 32(1).
Wei, M., Bai, B., Sung, A. H., Liu, Q., Wang, J., & Cather, M. E. (2007). Predicting injection profiles using ANFIS. Information Sciences, 177(20), 4445-4461.
Yusof, N., Ahmad, N. B., Othman, M. S., & Mohammad, F. A. (2012). A concise fuzzy rule base to reason student performance based on rough-fuzzy approach. In Fuzzy inference system–theory and applications (pp. 63-82). INTECH Open Access Publisher.