Research on Commodity Sales Forecast of E-Commerce Enterprises Integrating CNN and WDF Model
To enhance the accuracy of sales prediction for e-commerce enterprises'merchandise sales data,which often exhibit large scale,high dimensionality,and non-linearity,the paper proposes a novel sales prediction method called con-volutional neural network integrated weighted deep forest(CNN-WDF).Firstly,a convolution neural network(CNN),which has the advantage of handling high-dimensional data,is used to extract features of high-dimensional data to reduce redundancy and model training complexity.Secondly,an improved weighted deep forest model(WDF)is proposed for commodity sales prediction,in which the weight of each subtree in each forest level is calculated based on its prediction accuracy.The model can not only improve the overall prediction accuracy,but also has the advantages of fewer hyperpa-rameters and stronger interpretability compared with traditional deep network models.The experimental results using Jing-dong commodity sales data show that the prediction accuracy of the CNN-WDF fusion model is significantly higher than that of other comparative models on the same datasets of different sizes.Moreover,the prediction accuracy of this model improves even more with the increase in dataset size.
commodity sales forecastdeep learningintegrated modelconvolutional neural networkweighted deep forest
万方数据
维普
