Data Mining Strategies for Gold Price Prediction Using Multi-Factorial Influences

Abstract

The prediction of gold prices is a critical endeavor in the realm of financial markets, given the precious metal's significance as a store of value and its susceptibility to various economic, geopolitical, and market influences. This research focuses on the application of machine learning (ML) models to predict gold rates by incorporating factors such as the dollar exchange rate, crude oil prices, and element prices. The objective is to utilize advanced ML algorithms, including regression, decision trees, and ensemble methods, to analyze and interpret the complex relationships between these key factors and gold prices. Through the use of robust ML models and historical data, the study aims to improve the accuracy of gold rate predictions. Additionally, the research seeks to explore the importance of features and interactions among variables to develop comprehensive predictive models. The utilization of ML techniques in this context is expected to provide a deeper understanding of the dynamic influences of the dollar exchange rate, crude oil prices, and element prices on gold rates, offering valuable insights for investment and risk management strategies in financial markets.