Date of Award

Spring 5-31-2021

Degree Type

Thesis

Degree Name

M.A.

Degree Program

Computer Science

Department

Computer Science

Major Professor

Dr. Tamjidul Hoque

Second Advisor

Dr. Mahdi Abdelguerfi

Third Advisor

Dr. Shaikh M Arifuzzaman

Abstract

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.

Rights

The University of New Orleans and its agents retain the non-exclusive license to archive and make accessible this dissertation or thesis in whole or in part in all forms of media, now or hereafter known. The author retains all other ownership rights to the copyright of the thesis or dissertation.

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