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Young-Ji Roh(노영지),Won-Yong Shin(신원용),Jun-Chae Na(나준채) 한국통신학회 2021 한국통신학회 학술대회논문집 Vol.2021 No.6
Accurate demand forecasting in the food-retail industry is a very important task since it can reduce the cost caused by either shortage or overflow of food materials. In this paper, we show a comparative analysis on food-retail demand forecasting using the following two time series deep learning models: long short-term memory (LSTM) and convolutional neural network (CNN)-LSTM models. Using a café point-of-sale (POS) dataset, it is demonstrated that the CNN-LSTM model has a marginal gain over the LSTM model in terms of prediction error.