Abstract
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.
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Acknowledgments
The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI UIDB/05757/2020 (DOI: 10.54499/UIDB/05757/2020) and UIDP/05757/2020 (DOI: 10.54499/UIDB/05757/2020), and SusTEC LA/P/0007/2021 (DOI: 10.54499/LA/P/0007/2020). This work has been supported by NORTE-01-0247-FEDER-072598 iSafety: Intelligent system for occupational safety and well-being in the retail sector. Inês Sena was supported by FCT PhD grant UI/BD/153348/2022.
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Vaz, C.B. et al. (2024). Predicting Retail Store Transaction Patterns: A Comparison of ARIMA and Machine Learning Models. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2024. Communications in Computer and Information Science, vol 2280. Springer, Cham. https://doi.org/10.1007/978-3-031-77426-3_18
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DOI: https://doi.org/10.1007/978-3-031-77426-3_18
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