Abstract
The stock has been a long-standing and potential investment field until now, attracting much investment in this field every year. In particular, favorite stocks such as Dow Jones Industrial Average (DJIA), Tesla Inc (TSLA), and Meta Platforms Inc (META) have attracted many investments in recent years. The volatility of stock prices is very unpredictable, causing many difficulties for investors in this field. Furthermore, this study uses artificial intelligence models such as Autoregressive Integrated Moving Average (ARIMA), Support Vector Regression (SVR), Linear Regression (LR), and Gated Recurrent Unit (GRU) to predict closing prices and opening prices of three stock DJIA, TSLA, and META. Furthermore, proposing hybrid methods of the above models to improve and improve the accuracy of stock price prediction. The comparison results will be based on three evaluation parameters: RMSE, MAE, and MAPE.
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This research is funded by Vietnam National University HoChiMinh City (VNU-HCM) under grant number DS2022–26-03.
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Thuan, N.D., Nhut, N.M., Huong, N.T.V., Uyen, D.V.P. (2022). Forecasting the Opening and Closing Price Trends of Stock Using Hybrid Models and Artificial Intelligence Algorithm. In: Dang, T.K., Küng, J., Chung, T.M. (eds) Future Data and Security Engineering. Big Data, Security and Privacy, Smart City and Industry 4.0 Applications. FDSE 2022. Communications in Computer and Information Science, vol 1688. Springer, Singapore. https://doi.org/10.1007/978-981-19-8069-5_36
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DOI: https://doi.org/10.1007/978-981-19-8069-5_36
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