References
- B. Li, X. Gui, and Q. Zhou, “Construction of Development Momentum Index of Financial Technology by Principal Component Analysis in the Era of Digital Economy,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/2244960.
- Y. Zhao, “A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/6210627.
- A. H. Dhafer et al., “Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators,” Comput. Intell. Neurosci., vol. 2022, 2022, doi: 10.1155/2022/9208640.
- S. K. Kumar et al., “Stock Price Prediction Using Optimal Network Based Twitter Sentiment Analysis,” Intell. Autom. Soft Comput., vol. 33, no. 2, pp. 1217–1227, 2022, doi: 10.32604/iasc.2022.024311.
- Z. Bao, Q. Wei, T. Zhou, X. Jiang, and T. Watanabe, “Predicting stock high price using forecast error with recurrent neural network,” Appl. Math. Nonlinear Sci., vol. 6, no. 1, pp. 283–292, 2021, doi: 10.2478/amns.2021.2.00009.
- G. A. Altarawneh, A. B. Hassanat, A. S. Tarawneh, A. Abadleh, M. Alrashidi, and M. Alghamdi, “Stock Price Forecasting for Jordan Insurance Companies Amid the COVID-19 Pandemic Utilizing Off-the-Shelf Technical Analysis Methods,” Economies, vol. 10, no. 2, 2022, doi: 10.3390/economies10020043.
- S. Hansun, A. Suryadibrata, and D. R. Sandi, “Deep Learning Approach in Predicting Property and Real Estate Indices,” Int. J. Adv. Soft Comput. its Appl., vol. 14, no. 1, pp. 60–71, 2022, doi: 10.15849/IJASCA.220328.05.
- D. S. N. Ulum and A. S. Girsang, “Hyperparameter Optimization of Long-Short Term Memory using Symbiotic Organism Search for Stock Prediction,” Int. J. Innov. Res. Sci. Stud., vol. 5, no. 2, pp. 121–133, 2022, doi: 10.53894/ijirss.v5i2.415.
- D. Satria, “Predicting Banking Stock Prices Using Rnn, Lstm, and Gru Approach,” Appl. Comput. Sci., vol. 19, no. 1, pp. 82–94, 2023, doi: 10.35784/acs-2023-06.
- W. Lu, J. Li, J. Wang, and S. Wu, “a Novel Model for Stock Closing Price Prediction Using Cnn- Attention-Gru-Attention,” Econ. Comput. Econ. Cybern. Stud. Res., vol. 56, no. 3, pp. 251–264, 2022, doi: 10.24818/18423264/56.3.22.16.
- M. Ratchagit and H. Xu, “A Two-Delay Combination Model for Stock Price Prediction,” Mathematics, vol. 10, no. 19, 2022, doi: 10.3390/math10193447.
- M. Mohtasham Khani, S. Vahidnia, and A. Abbasi, “A Deep Learning-Based Method for Forecasting Gold Price with Respect to Pandemics,” SN Comput. Sci., vol. 2, no. 4, pp. 1–12, 2021, doi: 10.1007/s42979-021-00724-3.
- A. Ntakaris, J. Kanniainen, M. Gabbouj, and A. Iosifidis, Mid-price prediction based on machine learning methods with technical and quantitative indicators, vol. 15, no. 6 June. 2020. doi: 10.1371/journal.pone.0234107.
- S. Mishra, T. Ahmed, V. Mishra, S. Bourouis, and M. A. Ullah, “An Online Kernel Adaptive Filtering-Based Approach for Mid-Price Prediction,” Sci. Program., vol. 2022, 2022, doi: 10.1155/2022/3798734.
- M. A. Ledhem, “Deep learning with small and big data of symmetric volatility information for predicting daily accuracy improvement of JKII prices,” J. Cap. Mark. Stud., vol. 6, no. 2, pp. 130–147, 2022, doi: 10.1108/jcms-12-2021-0041.
- N. Deepika and M. Nirapamabhat, “An optimized machine learning model for stock trend anticipation,” Ing. des Syst. d’Information, vol. 25, no. 6, pp. 783–792, 2020, doi: 10.18280/isi.250608.
- M. K. Daradkeh, “A Hybrid Data Analytics Framework with Sentiment Convergence and Multi- Feature Fusion for Stock Trend Prediction,” Electronics, vol. 11, no. 2, 2022, doi: 10.3390/electronics11020250.
- X. Teng, T. Wang, X. Zhang, L. Lan, and Z. Luo, “Enhancing Stock Price Trend Prediction via a Time-Sensitive Data Augmentation Method,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/6737951.
- C. Zhao, P. Hu, X. Liu, X. Lan, and H. Zhang, “Stock Market Analysis Using Time Series Relational Models for Stock Price Prediction,” Mathematics, vol. 11, no. 5, 2023, doi: 10.3390/math11051130.
- K. E. Rajakumari, M. S. Kalyan, and M. V. Bhaskar, “Forward Forecast of Stock Price Using LSTM Machine Learning Algorithm,” Int. J. Comput. Theory Eng., vol. 12, no. 3, pp. 74–79, 2020, doi: 10.7763/IJCTE.2020.V12.1267.
- L. Li and B. M. Muwafak, “Adoption of deep learning Markov model combined with copula function in portfolio risk measurement,” Appl. Math. Nonlinear Sci., vol. 7, no. 1, pp. 901–916, 2022, doi: 10.2478/amns.2021.2.00112.
- M. C. Lee, J. W. Chang, S. C. Yeh, T. L. Chia, J. S. Liao, and X. M. Chen, “Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies,” Neural Comput. Appl., vol. 34, no. 16, pp. 13267–13279, 2022, doi: 10.1007/s00521-021-06828-4.
- M. Diqi, “TwitterGAN: robust spam detection in twitter using novel generative adversarial networks,” Int. J. Inf. Technol., vol. 15, no. 6, pp. 3103–3111, 2023, doi: 10.1007/s41870-023-01352-1.
- E. K. Ampomah, G. Nyame, Z. Qin, P. C. Addo, E. O. Gyamfi, and M. Gyan, “Stock market prediction with gaussian naïve bayes machine learning algorithm,” Inform., vol. 45, no. 2, pp. 243–256, 2021, doi: 10.31449/inf.v45i2.3407.
- A. Y. Fathi, I. A. El-Khodary, and M. Saafan, “A Hybrid Model Integrating Singular Spectrum Analysis and Backpropagation Neural Network for Stock Price Forecasting,” Rev. d’Intelligence Artif., vol. 35, no. 6, pp. 483–488, 2021, doi: 10.18280/ria.350606.
- J. Zhang, “Forecasting of Musical Equipment Demand Based on a Deep Neural Network,” Mob. Inf. Syst., vol. 2022, 2022, doi: 10.1155/2022/6580742.