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All Journal Brilliance: Research of Artificial Intelligence
Salem, Abdorwf A Mohamed
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Decision Sciences, Operations Research & Management Mathematics Other

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Predictive Models for Interest Rate Forecasting Using Machine Learning: A Comparative Analysis and Practical Application Salem, Abdorwf A Mohamed; Albourawi, Amaal Jummah Abdullah
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4983

Abstract

Forecasting interest rates is a fundamental task in financial planning, investment strategies, and policy-making. Traditional statistical models, while widely used, often fail to adequately capture complex non-linear relationships and temporal dependencies inherent in financial data. This study addresses these limitations by exploring the potential of machine learning models to improve the accuracy and reliability of interest rate forecasts. The primary objective of this research is to evaluate and compare the performance of multiple machine learning models, including linear regression, support vector machines, and deep learning techniques, in predicting interest rate trends. Historical data spanning two decades was collected and preprocessed, ensuring data quality and consistency. The models were trained and tested on this dataset using well-defined evaluation metrics such as mean absolute error and root mean squared error to ensure robust performance assessments. The results revealed that machine learning approaches, particularly deep learning models, outperformed traditional methods in capturing complex patterns and delivering more accurate forecasts. The findings further discuss the practical implications of implementing machine learning techniques in real-world financial contexts, highlighting both opportunities and challenges. In conclusion, this study provides actionable insights and a robust framework for integrating machine learning into interest rate forecasting, contributing to the advancement of predictive modeling in finance.
Predictive Models for Interest Rate Forecasting Using Machine Learning: A Comparative Analysis and Practical Application Salem, Abdorwf A Mohamed; Albourawi, Amaal Jummah Abdullah
Brilliance: Research of Artificial Intelligence Vol. 4 No. 2 (2024): Brilliance: Research of Artificial Intelligence, Article Research November 2024
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v4i2.4983

Abstract

Forecasting interest rates is a fundamental task in financial planning, investment strategies, and policy-making. Traditional statistical models, while widely used, often fail to adequately capture complex non-linear relationships and temporal dependencies inherent in financial data. This study addresses these limitations by exploring the potential of machine learning models to improve the accuracy and reliability of interest rate forecasts. The primary objective of this research is to evaluate and compare the performance of multiple machine learning models, including linear regression, support vector machines, and deep learning techniques, in predicting interest rate trends. Historical data spanning two decades was collected and preprocessed, ensuring data quality and consistency. The models were trained and tested on this dataset using well-defined evaluation metrics such as mean absolute error and root mean squared error to ensure robust performance assessments. The results revealed that machine learning approaches, particularly deep learning models, outperformed traditional methods in capturing complex patterns and delivering more accurate forecasts. The findings further discuss the practical implications of implementing machine learning techniques in real-world financial contexts, highlighting both opportunities and challenges. In conclusion, this study provides actionable insights and a robust framework for integrating machine learning into interest rate forecasting, contributing to the advancement of predictive modeling in finance.
Co-Authors Albourawi, Amaal Jummah Abdullah