查詢結果分析
相關文獻
頁籤選單縮合
題 名 | A Comparative Study on the Forecast of Fresh Food Sales Using Logistic Regression, Moving Average and BPNN Methods |
---|---|
作 者 | Lee, Wan-i; Chen, Cheng-wu; Chen, Kung-hsing; Chen, Tsung-hao; Liu, Chia-chi; | 書刊名 | Journal of Marine Science and Technology |
卷 期 | 20:2 2012.04[民101.04] |
頁 次 | 頁142-152 |
分類號 | 496.34 |
關鍵詞 | Moving average; BPNN; Logistic regression; Fresh food in POS database; |
語 文 | 英文(English) |
英文摘要 | ABSTRACT 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 a revenue producer for convenience stores. An efficient forecasting model would be helpful to increase sales volume and reduce waste at such stores. The correctness of the prediction rate is a good way to compare the efficacy of different models which is the method used here. The research results reveal that LR performs better than the other methods although MA is better suited to the management of convenience stores. |
本系統中英文摘要資訊取自各篇刊載內容。