基于多层LSTM的电商商品销售预测

Sales Forecasting Method of E-Commerce Products Based on LSTM

随着互联网和电子商务平台的发展,目前我国已经成为最近几年全球最大的网络销售市场,网络购物这一消费模式也已经逐渐成为我国国民日常生活当中必不可少的一种生活方式。销售预测作为一种重要的应用性问题,能够为商家提供更加准确的以销定产,避免产品积压;合理管理产品库存,安排生产进度;及时的产品增补,避免供不应求等现象的产生。与传统的线下门店销售相比较,电子商务具有数据采集简易、数据处理迅速、数据量庞大等优势,可以利用这些优势更加便捷地进行商品销量预测,来为电商提供货备,营销策略等方面的调整策略。论文使用了某电商网店的历史销量数据,在Keras框架下搭建了三层LSTM神经网络模型,对比分析了CNN模型、传统的LSTM模型、ARMA-SVR组合模型,以及WaveNet-LSTM模型的预测的运算结果,得到新模型在预测准确度,训练效率等方面都有更大的优越性。

With the development of the Internet and e-commerce platforms, China has become the world’s largest online sales market in recent years. Online shopping, a consumption mode, has gradually become an indispensable way of life in the daily life of Chinese people. As an important application problem, sales forecast can provide more accurate sales target for merchants and avoid product overstocking. Manage product inventory reasonably and arrange production schedule;Timely product supplement, to avoid the phenomenon of short supply. Compared with the traditional offline store sales, e-commerce has the advantages of simple data collection, rapid data processing and huge data volume. We can make use of these advantages to forecast the sales volume of goods more quickly, so as to provide e-commerce with inventory, marketing strategy and other adjustment strategies. This paper uses the historical sales data of an e-commerce shop to build a three-layer LSTM network model under the framework of Keras. The calculation results of CNN model, traditional LSTM model, ARMA-SVR combined model and WaveNet-LSTM model are compared and analyzed, and the prediction accuracy, training efficiency and so on of the new model obtained have greater superiority.