Date of Award
Dr. Tamjidul Hoque
Dr. Mahdi Abdelguerfi
Dr. Shaikh M Arifuzzaman
To encourage proper employee scheduling for managing crew load, restaurants have a need for accurate sales forecasting. We predict partitions of sales days, so each day is broken up into three sales periods: 10:00 AM-1:59 PM, 2:00 PM-5:59 PM, and 6:00 PM-10:00 PM. This study focuses on the middle timeslot, where sales forecasts should extend for one week. We gather three years of sales between 2016-2019 from a local restaurant, to generate a new dataset for researching sales forecasting methods.
Outlined are methodologies used when going from raw data to a workable dataset. We test many machine learning models on the dataset, including recurrent neural network models. The test domain is extended by considering methods which remove trend and seasonality. The best model for one-day forecasting regression is ridge with an MAE of 214, and the best for one-week forecasting is the temporal fusion transformer with an MAE of 216.
Schmidt, Austin B., "Machine Learning based Restaurant Sales Forecasting" (2021). University of New Orleans Theses and Dissertations. 2876.