References
[1]. Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
[2]. Iyiola, O., Munirat, Y., & Nwufo, C. (2012). The modern portfolio theory as an investment decision tool. Journal of Accounting and Taxation, 4(2), 19-28.
[3]. Manuca, R., & Savit, R. (1996). Stationarity and nonstationarity in time series analysis. Physica D: Nonlinear Phenomena, 99(2-3), 134-161.
[4]. Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126-139.
[5]. Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005-2019. Applied soft computing, 90, 106181.
[6]. Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26.
[7]. Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia Computer Science, 132, 1351-1362.
[8]. Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, March 26-28, Cambridge, United Kingdom, pp. 106-112.
[9]. Wang, J. J., Wang, J. Z., Zhang, Z. G., & Guo, S. P. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758-766.
[10]. Majumder, M. M. R., & Hossain, M. I. (2019). Limitation of ARIMA in extremely collapsed market: A proposed method. In 2019 International Conference on Electrical, Computer and Communication Engineering, February 07-09, Cox's Bazar, Bangladesh, pp. 1-5.
[11]. Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, 150199-150212.
[12]. Waqar, M., Dawood, H., Guo, P., Shahnawaz, M. B., & Ghazanfar, M. A. (2017). Prediction of stock market by principal component analysis. In 2017 13th International Conference on Computational Intelligence and Security, December 15-18, Hong Kong, China, pp. 599-602.
[13]. Huang, C. F. (2012). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing, 12(2), 807-818.
[14]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
[15]. Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 international conference on advances in computing, communications and informatics, September 13-16, Manipal, India, pp. 1643-1647.
[16]. Lu, W. J., Li, J. Z., Wang, J. Y., & Qin, L. L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33(10), 4741-4753.
[17]. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
[18]. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
[19]. Alam, M. D., & Uddin, G. (2009). Relationship between interest rate and stock price: empirical evidence from developed and developing countries. International Journal of Business and Management, 4(3), 43-51.
Cite this article
Li,J. (2023). Integrative forecasting and analysis of stock price using neural network and ARIMA model. Applied and Computational Engineering,6,969-981.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Montgomery, D. C., Jennings, C. L., & Kulahci, M. (2015). Introduction to time series analysis and forecasting. John Wiley & Sons.
[2]. Iyiola, O., Munirat, Y., & Nwufo, C. (2012). The modern portfolio theory as an investment decision tool. Journal of Accounting and Taxation, 4(2), 19-28.
[3]. Manuca, R., & Savit, R. (1996). Stationarity and nonstationarity in time series analysis. Physica D: Nonlinear Phenomena, 99(2-3), 134-161.
[4]. Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126-139.
[5]. Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005-2019. Applied soft computing, 90, 106181.
[6]. Shah, D., Isah, H., & Zulkernine, F. (2019). Stock market analysis: A review and taxonomy of prediction techniques. International Journal of Financial Studies, 7(2), 26.
[7]. Hiransha, M., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2018). NSE stock market prediction using deep-learning models. Procedia Computer Science, 132, 1351-1362.
[8]. Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014). Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th international conference on computer modelling and simulation, March 26-28, Cambridge, United Kingdom, pp. 106-112.
[9]. Wang, J. J., Wang, J. Z., Zhang, Z. G., & Guo, S. P. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758-766.
[10]. Majumder, M. M. R., & Hossain, M. I. (2019). Limitation of ARIMA in extremely collapsed market: A proposed method. In 2019 International Conference on Electrical, Computer and Communication Engineering, February 07-09, Cox's Bazar, Bangladesh, pp. 1-5.
[11]. Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, 150199-150212.
[12]. Waqar, M., Dawood, H., Guo, P., Shahnawaz, M. B., & Ghazanfar, M. A. (2017). Prediction of stock market by principal component analysis. In 2017 13th International Conference on Computational Intelligence and Security, December 15-18, Hong Kong, China, pp. 599-602.
[13]. Huang, C. F. (2012). A hybrid stock selection model using genetic algorithms and support vector regression. Applied Soft Computing, 12(2), 807-818.
[14]. Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
[15]. Selvin, S., Vinayakumar, R., Gopalakrishnan, E. A., Menon, V. K., & Soman, K. P. (2017). Stock price prediction using LSTM, RNN and CNN-sliding window model. In 2017 international conference on advances in computing, communications and informatics, September 13-16, Manipal, India, pp. 1643-1647.
[16]. Lu, W. J., Li, J. Z., Wang, J. Y., & Qin, L. L. (2021). A CNN-BiLSTM-AM method for stock price prediction. Neural Computing and Applications, 33(10), 4741-4753.
[17]. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324.
[18]. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: forecasting and control. John Wiley & Sons.
[19]. Alam, M. D., & Uddin, G. (2009). Relationship between interest rate and stock price: empirical evidence from developed and developing countries. International Journal of Business and Management, 4(3), 43-51.