Sliding Window-Based Randomized K-Fold Dynamic ANN for Next-Day Stock Trend Forecasting
The integration of machine learning and stock forecasting is attracting increased curiosity owing to its growing significance. This paper presents two main areas of study: predicting pattern trends for the next day and forecasting opening and closing prices using a new method that adds a dynamic hid...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Computation |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-3197/13/6/141 |
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| Summary: | The integration of machine learning and stock forecasting is attracting increased curiosity owing to its growing significance. This paper presents two main areas of study: predicting pattern trends for the next day and forecasting opening and closing prices using a new method that adds a dynamic hidden layer to artificial neural networks and employs a unique random <i>k</i>-fold cross-validation to enhance prediction accuracy and improve training. To validate the model, we are considering APPLE, GOOGLE, and AMAZON stock data. As a result, low root mean squared error (1.7208) and mean absolute error (0.9892) in both training and validation phases demonstrate the robust predictive performance of the dynamic ANN model. Furthermore, high R-values indicated a strong correlation between the experimental data and proposed model estimates. |
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| ISSN: | 2079-3197 |