基于卷积神经网络的在线产品销量预测分析研究

研究针对在线产品销售的决策需求,结合各行业在线产品的销量影响因素及深度学习算法优势,构建了适用于在线产品的销量预测模型,并重点评估了模型在不同种类在线产品上的适应性。研究过程将全连接模型与CNN的训练结果进行了对比,证明了CNN模型的精度和泛化能力。通过选取非深度学习模型Adaboosting作为对比基线,证明CNN模型在不同类别产品下的性能优势。另外,实验得出经过无监督预训练的CNN模型在销量预测问题上更有效、适应能力更强的结论。

On Suitability of Online Product Sales Prediction Model Based on Convolutional Neural Networks

Targeting at decision-making requirements of online product sales, by combining the influence factors of online product sales and the advantages of deep learning, we construct a sales prediction model for all online products, and mainly evaluate the suitability of the prediction model on different kinds of online products. By comparing the training results of CNN and full-connection network, we have proved the accuracy and generalization ability of this very model. By selecting non-deep learning model Adaboosting as the comparison baseline, we have certified the performance advantage of CNN on different kinds of online products, Moreover, the results show that the CNN, through unsupervised pre-training, is more effective on predicting the sales of online products and its suitability is greater than other models.