Exploring the Abilities and Limitations of the SARIMA Model For Retail Demand Forecasting on Dillard’s Data From 2021-2025

Author

• Victoria Q. Do, University of Arkansas, FayettevilleFollow

Date of Graduation

5-2027

Document Type

Thesis

Degree Name

Bachelor of Science in Data Science

Degree Level

Undergraduate

Department

Data Science

Advisor/Mentor

Karl Schubert

Committee Member

Lenin Rathinasamy

Second Committee Member

Giovanni Petris

Abstract

Retail demand forecasting is important for businesses such as Dillard’s, a leading American department store chain, because it allows for data-driven decisions that enhances profitability, efficiency, and customer satisfaction. These forecasts serve as a strategic foundation for various business aspects from inventory management and supply chain planning to marketing campaigns and more.  Forecasting methods vary widely from traditional to advanced machine learning models, each offering their pros and cons. This thesis will explore the ability and limitations of the seasonal autoregressive integrated (SARIMA) model to forecast demand on nearly four years of product group data provided by Dillard’s.

Keywords

data science, statistics, SARIMA

Citation

Do, V. Q. (2027). Exploring the Abilities and Limitations of the SARIMA Model For Retail Demand Forecasting on Dillard’s Data From 2021-2025. Data Science Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/dtscuht/30