A Development of Price Forecasting System by Neural Network

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The aim of this research is to apply the neural network information processing theory to an analysis of demand structure of fruits and vegetables and examine the ability of neural network in non-linear analysis and its internal representations. Neural network has the ability to reproduce the input-output relationship, which generally hard to be understood, by appropriate learning process. After learning, the neural network used to be applied to simulation because it has obtained enough knowledge about sample data. But if the neural network can not make the internal representations of sample data precisely, there will be no grounds for simulation.We used MR neural network which is a kind of modified feed-forward neural network and acts like Multiple Regression analysis. For the analysis, we input the quantity forwarded from six producing areas including Tottori prefecture and forwarding time as input data, and the actual prices of the vegetables produced in Tottori as teaching data, so that it could learn the relationship of these data. After the learning ended, we simulated the relation between quantity of forwarded and price.The obtained results represented non-linearity of the demand structure. the internal representations of the neural network showed the demand structure of fruits and vegatables precisely. Thus, the non-linearity of the demand structure was given logical grounds.The results shows that the price of vegetables has no linear relationship with quantity.