  This study introduces an innovative Gaussian Process (GP) model utilizing an
ensemble kernel that integrates Radial Basis Function (RBF), Rational
Quadratic, and Mat\'ern kernels for product sales forecasting. By applying
Bayesian optimization, we efficiently find the optimal weights for each kernel,
enhancing the model's ability to handle complex sales data patterns. Our
approach significantly outperforms traditional GP models, achieving a notable
98\% accuracy and superior performance across key metrics including Mean
Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE),
and Coefficient of Determination ($R^2$). This advancement underscores the
effectiveness of ensemble kernels and Bayesian optimization in improving
predictive accuracy, offering profound implications for machine learning
applications in sales forecasting.
