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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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
M. Ratchagit and H. Xu, “A Two-Delay Combination Model for Stock Price Prediction,” Mathematics, vol. 10, no. 19, 2022, doi: 10.3390/math10193447.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
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.Search in Google Scholar
J. Zhang, “Forecasting of Musical Equipment Demand Based on a Deep Neural Network,” Mob. Inf. Syst., vol. 2022, 2022, doi: 10.1155/2022/6580742.Search in Google Scholar
