Forecasting of commercial sales with large scale Gaussian Processes

TL;DR

This paper explores the application of large-scale Gaussian Processes for forecasting sales in the fast moving consumer goods industry, highlighting their potential for automatic feature relevance and data insights despite challenges with large, high-dimensional data.

Contribution

It reviews and evaluates Gaussian Process modeling approaches for large commercial sales data, demonstrating their usefulness as decision-making tools.

Findings

Gaussian Processes can effectively model large sales data.

Automatic feature relevance improves model interpretability.

Models provide valuable insights for management decisions.

Abstract

This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.