📅 17 March 2024
DOI: 10.62951/ijamc.v1i1.3
A Comparative Analysis of Machine Learning Models for Predictive Analytics in Finance
International Journal of Applied Mathematics and Computing
Asosiasi Riset Ilmu Matematika dan Sains Indonesia (ARIMSI)
📄 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.
🔖 Keywords
#Machine learning; predictive analytics; time-series analysis
#financial modeling
#comparative analysis.
ℹ️ Informasi Publikasi
Tanggal Publikasi
17 March 2024
Volume / Nomor / Tahun
Volume 1, Nomor 1, Tahun 2024
📝 HOW TO CITE
Jose Miguel Reyes; Lea Patricia Santos; Antonino Perez, "A Comparative Analysis of Machine Learning Models for Predictive Analytics in Finance," International Journal of Applied Mathematics and Computing, vol. 1, no. 1, Mar. 2024.
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