Accurate and correct price prediction of gold is significant to maintain the stability and fair operation of gold markets as well as the efficiency and proper functioning of financial markets related to gold trading. Gold prices have a long history of non-linear and non-stationary behavior that generates highly complex datasets which are very difficult to forecast with high accuracy and reliability. At the same time Support Vector Regression (SVR) has been utilized as a robust information for prediction not only gold prices but also overall metal price movements. First, the predictive capability of SVR is highly sensitive to hyperparameter selection which needs to be thoroughly tuned and optimized for potentially optimal performance. Therefore, the suitable and cautious selection of these hyperparameters can play a significant and direct role in determining the success and precision of SVR. In this study, the focus is on proposing a new technique through horse herd optimization (HHO), which can be used for hyperparameter estimation and tuning of support vector regression (SVR) based on the collective behavior of horse herds to achieve efficient forecasting accuracy and reliability in gold prices. The outcomes and conclusions illustrate that the hybrid algorithm can furnish an increase to the general accuracy of our gold prices, showing superior overall performance as in contrast with two classical benchmark techniques on this area. These enhancements are time to market and useful for enabling faster global economic regeneration and restoration, but also enable the demand side characteristics of integrating international trade commodity price discovery mechanisms into explanations for AI techniques in the context of learning tasks and financial oscular analytics.
