Analysis of Marketing Prediction Model Based on Genetic Neural Network: Taking Clothing Marketing as an Example

Hua Peng, Luxiao Dong, Yi Sun, Yanfang Jiang and Miaochao Chen

Journal of Mathematics, 2022, vol. 2022, 1-14

Abstract: With the economic and social development and the improvement of people’s living standards, consumers have put forward higher requirements for clothing from quantity to quality. The clothing industry has ushered in vigorous vitality and broad development space. China’s clothing industry has achieved great results after long-term development. With the gradual abolition of world textile and apparel trade quotas, China’s apparel and textile industry is facing greater opportunities and challenges. In today’s increasingly developing market economy, many production companies are making marketing forecasts. A good forecast result can be used to guide the company’s decision-making. The results of the model help decision-makers to reasonably arrange production and formulate marketing strategies. With the development of genetic neural network technology, this technology has been more and more widely used in signal processing, pattern recognition and other application fields. This article discusses a marketing forecasting model based on genetic neural network, predicting model parameters based on historical data of actual sales, and then carrying out experimental analysis. First of all, a series of analysis and preprocessing must be performed on the collected data. In the process of estimating and calculating the parameters of the prediction model, an error criterion is selected to determine a set of relatively optimal prediction parameters, and finally the model results A verification analysis was carried out. The experimental results show that the genetic neural network method can be used to establish a marketing forecasting model, and the established forecasting model has certain practical application value.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:8743568

DOI: 10.1155/2022/8743568

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