Meal participation prediction with bayesian hierarchical models

Abstract

Forecasting sales in the catering industry helps authorities to organize daily transactions efficiently to prevent both waste and business loss. In this study, we focused on predicting meal sales in Bilintur Catering Centre with the dataset which is collected through five academic years. To forecast the meal sales, we constructed two Bayesian hierarchical models. The first model does not differentiate effects of predictors in different academic years, while the second does. We derived the full conditional distributions and employed Gibbs sampling in an extensive MCMC study. We tested two models along with a benchmark multiple regression model on the held-out academic year. We concluded that multiple regression and first model provide more accurate results.