PRODUCT PRICE FORECASTING BASED ON CORRELATIVE PRICE NET AND NEURAL NETWORKS - Yan, Hong-Sen; Jiang, Nan-Yun; Shi, Wen-Wu; Meng, Xian-Gang; Jiang, Tian-Hua (International Journal of Industrial Engineering: Theory, Applications and Practice, 2017)
The manufacturing enterprise need price forecasting in order to attain maximum profit and guild production. However, it is difficult to trace the changing rules of their historical data since product price may fluctuate abruptly. Therefore, a price forecasting method based on correlative price net and neural networks is proposed in the paper. Firstly, by analyzing the main factors to product price changes, a model of correlative price net (CPN) that connects many products whose prices affect each other is set up. Then, the theoretical proof that the whole CPN can be substituted by its correlative price sub-net for price forecasting is provided. Based on the correlative price sub net, a set of BP neural networks are introduced for building a new network, named the price forecasting net (PFN), which can reflect the factors of price fluctuation in the correlative price sub net. Finally, since the change of factors in the correlative price sub net relates to the variation of product price caused by it, the product price can be forecasted on basis of the change of factors in the correlative price sub net by means of PFN. The theoretic analysis and simulation experiment show that the proposed method adapts well to product price forecasting, especially when prices change abruptly.