<![CDATA[Amidst global economic volatility, accurate forecasting of gold prices remains a crucial and challenging task for investors and financial policymakers, as gold functions as a vital safe-haven asset and a hedge against inflation. This study focuses on gold price prediction utilizing the Support Vector Regression (SVR) algorithm, with the main objective of improving forecast accuracy. The relevance of this prediction is underpinned by the dynamic characteristics of gold prices, which is essential for decision-making by various stakeholders. Historical gold price data were obtained from the investing.com platform. The SVR implementation was carried out utilizing the Radial Basis Function (RBF) kernel. The SVR parameter optimization process employing Grid Search successfully identified the optimal values, namely C=1000, ϵ=0.5, and γ=0.01. To ensure model robustness and generalization capability, validation was performed using 5-Fold Cross Validation, which yielded an average Mean Absolute Percentage Error (MAPE) of 0.66%. The very high level of SVR accuracy, alongside its consistency across each fold, stability, and reliability, indicates that the optimized SVR model is a prospective solution for gold price forecasting in the commodity market.]]>
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