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A Comparative Study On The Forecast Of Fresh Food Sales Using Logistic Regression, Moving Average And Bpnn Methods, Wan-I Lee, Cheng-Wu Chen, Kung-Hsing Chen, Tsung-Hao Chen, Chia-Chi Liu

Journal of Marine Science and Technology

Different prediction methods give different performance predictions when used for daily fresh food sales forecasting. Logistic Regression (LR) is a good choice for binary data, the Moving Average (MA) method is good for simple prediction, while the Back-Propagation Neural Network (BPNN) method is good for long term data. In this study we develop and compare the performance of three sales forecasting models, based on the above three prediction methods, for the forecasting of fresh food sales in a point of sales (POS) database for convenience stores. Fresh food is characterized by two factors: its short shelf-life and its importance as …