Gold Price Forecasting Based on Regularized Linear Models and Extreme Gradient Boosting

Precise predictions of gold prices are crucial for investors, decision-makers, and financial analysts, given that gold functions as both a secure asset and an important economic indicator. The research showcased introduces a machine learning framework that employs Bayesian optimization, XG Boost, and Lasso regression to enhance prediction efficiency and accuracy. An examination was performed on historical gold price data along with relevant financial information indicators, utilizing Lasso regression to determine the key predictors via L1 regularization, whereas XG Boost successfully complex non-linear interactions in the market were identified. Bayesian optimization was employed to optimize the model's hyperparameters, eliminating the need for manual tuning and ensuring optimal performance The comparative evaluation indicates that the integration of Lasso regression provides a simple and effective method for feature selection, whereas XG Boost employs gradient boosting to uncover intricate patterns within the data, leading to lower error rates and enhanced generalization. By integrating these methods, it creates a powerful, data-driven forecasting tool that delivers reliable and accurate predictions the rapidly changing financial environment. The results emphasize the importance of merging sophisticated machine learning with automated optimization to ensure accurate commodity price forecasts and support strategic decision-making in both investment and policy areas.