Enhancing Predictive Accuracy in Pharmaceutical Sales Through An Ensemble Kernel Gaussian Process Regression Approach

TL;DR

This paper introduces an ensemble kernel Gaussian Process Regression method that combines multiple kernels to significantly improve predictive accuracy in pharmaceutical sales data analysis.

Contribution

It presents a novel ensemble kernel approach for GPR, optimized via Bayesian methods, achieving near-perfect predictive performance on complex datasets.

Findings

Ensemble kernel GPR outperforms single kernel models.

Achieved near 1.0 R^2 score in predictions.

Significantly reduced error metrics.

Abstract

This research employs Gaussian Process Regression (GPR) with an ensemble kernel, integrating Exponential Squared, Revised Mat\'ern, and Rational Quadratic kernels to analyze pharmaceutical sales data. Bayesian optimization was used to identify optimal kernel weights: 0.76 for Exponential Squared, 0.21 for Revised Mat\'ern, and 0.13 for Rational Quadratic. The ensemble kernel demonstrated superior performance in predictive accuracy, achieving an \( R^2 \) score near 1.0, and significantly lower values in Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). These findings highlight the efficacy of ensemble kernels in GPR for predictive analytics in complex pharmaceutical sales datasets.