A Dynamic Feature Selection Technique for the Stock Price Forecasting
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-39774-5_81
Reference11 articles.
1. Yun, K.K., Yoon, S.W., Won, D.: Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection. Expert Syst. Appl. 213, 118803 (2023)
2. Kumari, B., Swarnkar, T.: Forecasting daily stock movement using a hybrid normalization based intersection feature selection and ANN. Procedia Comput. Sci. 218, 1424–1433 (2023)
3. Haq, A.U., Zeb, A., Lei, Z., Zhang, D.: Forecasting daily stock trend using multi-filter feature selection and deep learning. Expert Syst. Appl. 168, 114444 (2021)
4. Li, J., et al.: Feature selection: a data perspective. ACM Comput. Surv. (CSUR) 50(6), 1–45 (2017)
5. Huang, Q., Xia, T., Sun, H., Yamada, M., Chang, Y.: Unsupervised nonlinear feature selection from high-dimensional signed networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4182–4189. AAAI Press, USA (2020)
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