Product sale prediction method based on support vector machine model with parameter optimization

Abstract

The invention relates to a product sale prediction method based on a support vector machine model with parameter optimization. The method is characterized by including the following steps: S1, selecting a kernel function of the support vector machine; S2, adopting a grid search method to optimize predetermined parameters in the kernel function in S1; S3, establishing a prediction model; and S4, predicting a product sale trend, and applying historical product sales data to the prediction model in S3 to obtain a prediction result of product sale. The method provided by the invention greatly improves precision of sale prediction through a machine learning method, the SVM prediction model is short in prediction time, high in prediction precision and strong in robustness, avoids the circumstance that part of nonlinear models are easy to fall in the defects of local minimums and slow convergence rate, and thus the prediction model based on SVM optimization is effective and feasible. The prediction method provided by the invention overcomes the defects of poor precision and low calculation efficiency in traditional sale prediction, can provide a relatively accurate sale prediction reference for a decision-making level, and has good application value.

Description

technical field [0001] The invention relates to a forecasting method, in particular to a product sales forecasting method based on a parameter-optimized support vector machine model. Background technique [0002] With the rapid development of science and technology in the 21st century, people's living standards have been greatly improved. More and more families buy cars as a means of transportation. my country's auto market has entered the era of brand marketing, and market competition has also shifted from traditional product and price competition to Brand and channel competition. If automobile manufacturing enterprises can realize quantitative forecasting in production, manufacturing, sales and other links, and provide necessary basis for their decision-making, they can take the lead in the increasingly fierce market competition while meeting the individual needs of customers. [0003] However, most of the current automobile sales forecasting methods use artificial statist...

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Problems solved by technology

[0004] The present invention is carried out in order to solve the above-mentioned problem, therefore, one aspect of the present invention adopts this advanced machine learning method of SVM (Support Vector Machine), under the situation that does not increase parameter and a small amount of model complexity as far as possible, to Prediction model is optimized, in order to realize the sales forecast of more accurate automobile product, the object of one aspect of the present invention is to provide a kind of sales forecasting method based on the support vector machine of parameter optimization

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