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All Journal International Journal of Applied Mathematics and Computing.
Antonino Perez
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Computer Science & IT Mathematics

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A Comparative Analysis of Machine Learning Models for Predictive Analytics in Finance Jose Miguel Reyes; Lea Patricia Santos; Antonino Perez
International Journal of Applied Mathematics and Computing Vol. 1 No. 1 (2024): January: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v1i1.3

Abstract

This paper compares various machine learning models in their ability to predict financial trends, with a focus on time-series analysis. We evaluate models such as linear regression, decision trees, support vector machines, and deep learning, measuring their performance based on accuracy, computational cost, and interpretability. Our results reveal that deep learning models offer superior accuracy but are less interpretable, while simpler models, though less accurate, provide better insight into the underlying data. This research provides guidelines for selecting suitable models based on specific financial applications.
Co-Authors Jose Miguel Reyes Lea Patricia Santos