Product regional pricing method based on gray neural network model

Abstract

The invention discloses a product regional pricing method based on a gray neural network model. The method comprises the steps of obtaining consumer price index data over the years and product regional price indexes over the years; performing interval average processing on the product regional price indexes over the years to obtain an average product price index; performing the best translationaltransformation on the consumer price index data over the years and data fitting with a continuous gray model to construct a continuous consumer price index GM (1,1) model over the years; obtaining theconsumer price index training data, performing network training on the consumer price index training data through the continuous consumer price index GM(1,1) model over the years and the average product price index and establishing the neural network model; using the continuous consumer price index GM(1,1) model over the years for prediction to obtain predicted consumer price indexes in the nextyear, substituting the predicted consumer price indexes in the next year into the neural network model and performing calculation to obtain product regional price indexes in the next year.

Description

technical field [0001] The invention relates to the technical field of price forecasting, in particular to a product regional pricing method based on a gray neural network model. Background technique [0002] The price of a product is not only the source of a company's sales and profits, but also the key to a product's survival (success). Usually, the price of a product is determined by the purchasing power of the market, and the consumption level of residents is a good reflection of the purchasing power of the market. Differences in the consumption level of residents in different regions will inevitably lead to inconsistencies in the pricing of the same product. Therefore, how to consider the consumption of regional residents, It has become an important task for the enterprise to give the product an appropriate price so that the product can quickly obtain a better market. [0003] At present, from the perspective of regional residents' consumption, the common methods for c...

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Problems solved by technology

The cluster analysis method can analyze the prices of residents' various consumptions to determine the pricing range of products, but it cannot predict the pricing of products well; the regression model is relatively simple to calculate and easy to implement. The disadvantage is the multiple regression problem for multiple consumptions , the prediction accuracy is difficult to control; the prediction results of optimization models such as game theory and goal planning can reach the optimum, and it is also the model most used in research experiments at present, but the calculation is relatively complicated, and a continuous value space is required to solve the model

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