Application of Time Series and Neural Network Methods for Sales Prediction Model

• Main Authors: Lee , Yue-Hua, 李玉華
• Other Authors: Chang , She-I
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/r5w4a6

碩士 === 國立中正大學 === 會計與資訊科技研究所 === 103 === In hot summer, tea is one of the main sales in beverage, and drinking tea is becoming one of daily necessities today. Such a habit of drinking tea is quietly integrated into our lives. Because of changes in behavior of consumers in the market, convenience stores have become the main channel for selling tea. Today the speed of modern sales channel and the dividing profits by distributors have changed the manufacturers’ attitude to put effort on reducing the cost of sales in order to enhance their revenue. Therefore developing a production plan has become the important issue for the manufacturers. A well- developed production plan not only can reduce the production cost of tea, but also can enhance coordination between machines and employees, resulting in little waste of production resources. A good production plan relies on the accurate prediction of tea sales that is mainly dependent on consumer behaviors. However, the relationship among sale of products, consumers and other products is very complex. To address the issue about the prediction of tea sales, the traditional data analysis is not easy to predict the sales in the future market. Therefore manufacturers are looking forward to a better decision support systems for prediction. This study used tea sales data from point of sale (POS) in convenience store chain. We used data mining methodology to construct sales prediction model. The prediction method in the model is based on statistical time series moving average (MA) and autoregression integrated moving average (ARIMA). We also used neural network based on back-propagation network with changes in causal parameters for prediction models: One includes Granger causality test and the neural network ; another one includes Granger causality test, the neural network and more, the autoregressive (AR). We used actual sales data to assess these prediction models. Our results have shown that using Granger causality test plus autoregressive within back-propagation neural networks has the best prediction for these data.