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English Information

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Authors
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1 Orlando Junior(orlandodasilvajr@gmail.com)
2 Osvaldo Ribeiro(osvaldoribeiro70@gmail.com)

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Reference
# Reference
1 Banas, J. and Utnik-Banas, K. (2021). Evaluating a seasonal autoregressive moving average model with an exogenous variable for short-term timber price forecasting. Forest Policy and Economics, v. 131.
2 Bhandari, H. N., Rimal, B., Pokhrel N. R., Rimal, R., Dahal K. R. and Khatri, R. K. C. (2022). Predicting stock market index using LSTM. Machine Learning with Applications.
3 De Campos, L. M. L. and De Figueiredo, Y. F. C. (2021). Avaliação de redes neurais profundas para a previsão de preço das ações da Petrobrás. Revista Gestão & Tecnologia, [S. l.], v. 21, n. 3.
4 Hu, Z., Zhao, Y. and Khushi, M. (2021). A survey of forex and stock price prediction using deep learning. Applied System Innovation.
5 Lakshminarayanan, S. K. and McCrae, J. P. (2019). A Comparative Study of SVM and LSTM Deep Learning Algorithms for Stock Market Prediction. Artificial Intelligence and Cognitive Science (AICS).
6 Long, W., Lu Z. and Cui, L. (2019). Deep learning-based feature engineering for stock price movement prediction. Knowledge-Based Systems, v. 164.
7 Nelson, D. M.Q., Pereira, A. C. M. and De Oliveira, R. A. (2017). Stock market's price movement prediction with LSTM neural networks. International Joint Conference on Neural Networks (IJCNN).
8 Olivares, K. G. et al. (2023). Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx. International Journal of Forecasting.
9 Patel, J., Shah, S., Thakkar, P. and Kotecha, K. (2015). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications.
10 Yuan, X., Yuan J. and Ain, Q. UI (2020) Integrated long-term stock selection models based on feature selection and machine learning algorithms for China stock market. IEEE Access, v. 8.