Foreign Commodity Sales Forecast Based on Model Fusion

In era of big data, global shopping is very popular. Different from domestic shopping, global shopping requires the merchants to purchase goods from abroad in advance, thus knowing approximate sales of goods in the next period will be very helpful for merchants in determining the procurement amount. To solve this problem, in this paper, we are based on user performance data on commodity, commodity sales data and commodity promotion data to predict the commodity sales after 45 days. We extract two kinds of features, statistical feature and discrete feature and design a fusion model. This fusion model uses three different models, which is LR (Linear regression), XGBoost (Extreme Gradient Boosting) and LightGBM (Light Gradient BoostingMachine). The experimental results show that the prediction results of this model have a less bias compared to real value. It will provide some guidance for the decision of merchants.