Subject Areas : Computer Engineering
1 - Department of Accounting, Azadshahr Branch, Islamic Azad University, Azadshahr, Iran.
Keywords:
Abstract :
[1] Chhajer, P., Shah, M. and Kshirsagar, A., 2022. The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2, p.100015.
[2] Gandhmal, D.P. and Kumar, K., 2019. Systematic analysis and review of stock market prediction techniques. Computer Science Review, 34, p.100190.
[3] Jakubowski, A. and Szewczak, Z.S., 2021. Truncated moments of perpetuities and a new central limit theorem for GARCH processes without Kesten’s regularity. Stochastic Processes and their Applications, 131, pp.151-171.
[4] Ding, Y., 2019, February. Empirical Analysis of Logarithmic Return Rate of China’s Financial Stocks—based on the ARMA-GARCH Model. In 2018 International Symposium on Social Science and Management Innovation (SSMI 2018) (pp. 286-290). Atlantis Press.
[5] Devi, B.U., Sundar, D. and Alli, P., 2013. An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50. International Journal of Data Mining & Knowledge Management Process, 3(1), p.65.
[6] Gu, F.J. and Cen, Z.D., 2011. Study on the volatility of Chinese Shanghai and Shenzhen stock markets with GARCH and SV Models. J Math Pract Theory, 1, pp.4-22.
[7] Xu, X.S. and Chen, Y.B., 2001. Empirical study on nonlinearity in China stock market. Quantitative and Technical Economics, 18, pp.110-113.
[8] Liu, W. and Morley, B., 2009. Volatility forecasting in the hang seng index using the GARCH approach. Asia-Pacific Financial Markets, 16, pp.51-63.
[9] Yuan, S.L., Lu, H.M., Wang, P.P., Tian, C.G. and Gao, Z.J., 2021. Boron removal from metallurgical grade silicon by slag refining based on GA-BP neural network. Rare Metals, 40, pp.237-242.
[10] Aras, S. and Kocakoç, İ.D., 2016. A new model selection strategy in time series forecasting with artificial neural networks: IHTS. Neurocomputing, 174, pp.974-987.
[11] Raut, R.D., Priyadarshinee, P., Gardas, B.B. and Jha, M.K., 2018. Analyzing the factors influencing cloud computing adoption using three stage hybrid SEM-ANN-ISM (SEANIS) approach. Technological Forecasting and Social Change, 134, pp.98-123.
[12] Richhariya, B. and Tanveer, M., 2018. EEG signal classification using universum support vector machine. Expert Systems with Applications, 106, pp.169-182.
[13] Atmaja, B.T. and Akagi, M., 2021. Two-stage dimensional emotion recognition by fusing predictions of acoustic and text networks using SVM. Speech Communication, 126, pp.9-21.
[14] Fan, L., Pan, S., Li, Z. and Li, H., 2016. An ICA-based support vector regression scheme for forecasting crude oil prices. Technological Forecasting and Social Change, 112, pp.245-253.
[15] Zhang, G.P., 2003. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, pp.159-175.
[16] Chen, Y. and Hao, Y., 2018. Integrating principle component analysis and weighted support vector machine for stock trading signals prediction. Neurocomputing, 321, pp.381-402.
[17] Ghosh, A., Bose, S., Maji, G., Debnath, N. and Sen, S., 2019, September. Stock price prediction using LSTM on Indian Share Market. In Proceedings of 32nd international conference on (Vol. 63, pp. 101-110).
[18] Baek, Y. and Kim, H.Y., 2018. ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications, 113, pp.457-480.
[19] Wang, J. and Wang, J., 2017. Forecasting stochastic neural network based on financial empirical mode decomposition. Neural Networks, 90, pp.8-20.
[20] Bisoi, R., Dash, P.K. and Parida, A.K., 2019. Hybrid variational mode decomposition and evolutionary robust kernel extreme learning machine for stock price and movement prediction on daily basis. Applied Soft Computing, 74, pp.652-678.
[21] Yang, W., Peng, Z., Wei, K., Shi, P. and Tian, W., 2017. Superiorities of variational mode decomposition over empirical mode decomposition particularly in time–frequency feature extraction and wind turbine condition monitoring. IET Renewable Power Generation, 11(4), pp.443-452.
[22] Rawat, W. and Wang, Z., 2017. Deep convolutional neural networks for image classification: A comprehensive review. Neural computation, 29(9), pp.2352-2449.
[23] Krizhevsky, A., Sutskever, I. and Hinton, G.E., 2012. Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
[24] Hochreiter, S. and Schmidhuber, J., 1997. Long short-term memory. Neural computation, 9(8), pp.1735-1780.
