International Journal of Artificial Intelligence and Machine Learning
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Volume 4, Issue 2, July 2024 | |
Review ArticleOpenAccess | |
Optimization Algorithms in Deep Learning Models for Improving the Forecasting Accuracy in Sequential Datasets with Application in the South African Stock Market Index: A Review |
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1Benguela Global Fund Managers, Johannesburg, 2191, South Africa. E-mail: sanelemakamo26@gmail.com
*Corresponding Author | |
Int.Artif.Intell.&Mach.Learn. 4(2) (2024) 1-8, DOI: https://doi.org/10.51483/IJAIML.4.2.2024.1-8 | |
Received: 24/02/2024|Accepted: 19/06/2024|Published: 05/07/2024 |
In this paper we review different popular optimization algorithms for machine learning models, we then evaluate the model performance and convergence rates for each optimizer using a multilayer fully connected neural networks. Using sequential dataset of index returns (time-series data) spanning over of 20-years, we demonstrate Adam and RMSprop optimizers can efficiently solve practical deep learning problems dealing with sequential datasets. We use the same parameter initialization when comparing different optimization algorithms. The hyper-parameters, such as learning rate and momentum, are searched over a dense grid and the results are reported using the best hyperparameter setting.
Keywords: Machine learning, Deep learning, Neural networks, Optimization algorithms, Loss function
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