Time: Oct 1, 2021 08:30 AM Istanbul
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Meeting ID: 654 774 6234
M.S. THESIS PRESENTATION;Title: Meal Participation Prediction with Bayesian Hierarchical Models
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 differential 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 the effects of certain predictors change over time, hierarchical Bayes models can depict the change, and the natural regularization property of Bayesian approach gives more accurate and stable forecasts.