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Publication
Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning Models
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Advisor(s)
Abstract(s)
Retail transactions represent sales of consumer goods, or
final goods, by consumer companies. This sector faces security challenges
due to the hustle and bustle of sales, affecting employees’ workload. In
this context, it is essential to estimate the number of customers who
will appear in the store daily so that companies can dynamically adjust
employee schedules, aligning workforce capacity with expected demand.
This can be achieved by forecasting transactions using past observations
and forecasting algorithms. This study aims to compare the ARIMA time
series algorithm with several Machine Learning algorithms to predict the
number of daily transactions in different store patterns, considering data
variability. The study identifies four typical store patterns based on these
criteria using daily transaction data between 2019 and 2023 from all retail
stores of the leading company in Portugal. Due to data variability and
the results obtained, the algorithm that presents the most minor errors
in predicting daily transactions is selected for each store. This study’s
ultimate goal is to fill the gap in forecasting daily customer transactions
and present a suitable forecasting model to mitigate risks associated with
transactions in retail stores.
Description
Keywords
Forecasting Transactions Time series Machine Learning
Pedagogical Context
Citation
Vaz, Clara Bento; Sena, Inês; Braga, Ana Cristina; Novais, Paulo; Fernandes, Florbela P.; Lima, José; Pereira, Ana I. (2024). Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning Models. In Optimization, Learning Algorithms and Applications (OL2A 2024), Part I. Cham: Springer Nature. p. 268- 283. eISBN 978-3-031-77426-3
Publisher
Springer Nature