Optimisasi Hyperparameter BiLSTM Menggunakan Bayesian Optimization untuk Prediksi Harga Saham
The accuracy of deep learning models in predicting dynamic and non-linear stock market data highly depends on selecting optimal hyperparameters. However, finding optimal hyperparameters can be costly in terms of the model's objective function, as it requires testing all possible combinations of hype...
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| Principais autores: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
Department of Mathematics, Universitas Negeri Gorontalo
2025-02-01
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| Series: | Jambura Journal of Mathematics |
| Assuntos: | |
| Acceso en liña: | https://ejurnal.ung.ac.id/index.php/jjom/article/view/27166 |
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