Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2023.2223263
Improved optimization model for forecasting stock directions (FSD)
Noura Metawa
Iman Akour
Zahra Tarek
Mohamed Elhoseny
Sažetak
The study of stock market price predictions is very important. The
Recurrent Neural Network (RNN) has shown excellent results with
this issue. There are two significant problems with using this strategy.
One is that it constantly struggles with extensive neural network
construction efforts and hyper-parameter adjustments. Two,
it often fails to come up with a superior answer. The suggested
model is proposed to optimize the network topology and hyperparameters
of the RNN model. RNN is utilized for effective forecasting
of stock directions in this research. Additionally, the
Improved Differential Evolution (IDE) method is used to tune the
RNN’s hyperparameters to their best potential. Utilizing the IDE
method helps in achieving the best stock direction prediction
results possible. The direction of Stock Prediction (SP) changes
has been accurately predicted by the proposed model that is
being presented. A series of tests on two popular benchmark
datasets (AAPL and FB) revealed the superiority of the proposed
model over the other strategies with accuracy of 99.02% and the
loss close of 0.1% for training and testing.
Ključne riječi
Stock prices; prediction model; direction forecasting; deep learning; hyperparameter optimizers; differential evolution; RNN
Hrčak ID:
314897
URI
Datum izdavanja:
21.6.2023.
Posjeta: 67 *