Abstract
Sales forecasting of vegetables and fruits imposes a challenging task for the retailers because the demand for them varies depending on several factors, such as temperature, season, holiday. Poor sales forecasting can cause too much cost for retailers since these products are unusable after deterioration. Also, people tend to consume these products freshly. This research aims to compare the forecasting performance of traditional statistical and new machine learning methods. We apply seasonal ARIMA to forecast daily sales of fruits and vegetables as a traditional method. As a machine learning algorithm, we apply LSTM and XGBoost algorithms. The results indicate that the XGBoost algorithm gives more accurate results than the other two methods.
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Turgut, Y., Erdem, M. (2022). Forecasting of Retail Produce Sales Based on XGBoost Algorithm. In: Calisir, F. (eds) Industrial Engineering in the Internet-of-Things World. GJCIE 2020. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-76724-2_3
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DOI: https://doi.org/10.1007/978-3-030-76724-2_3
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