基于XGBoost和LSTM加权组合模型在销售预测的应用

针对多变量的商品销售预测问题，为了提高预测的精度，提出了一种ARIMA-XGBoost-LSTM加权组合方法，对具有多个影响因素的商品销售序列进行预测，本文采用ARIMA做单变量预测，将预测值作为新变量同其他变量一起放入XGBoost模型中进行不同属性的挖掘，并将XGBoost的预测值合并到多变量序列中，然后通过将新的多维数据转换为监督学习序列后利用LSTM模型进行预测，将3种模型预测结果进行加权组合，通过多次实验得出最佳组合的权值，以此计算出最终的预测值.数据结果表明，基于XGBoost和LSTM的加权组合的多变量预测方法比单一的预测方法所得到的预测值更为精准.

Application of Weighted Combination Model Based on XGBoost and LSTM in Sales Forecasting

Aiming at the multi-variable commodity sales forecasting problem, in order to improve the accuracy of prediction, an ARIMA-XGBoost-Lstm weighted combination method is proposed to predict the sales sequence of commodities with multiple influencing factors, In this study, ARIMA is used for univariate prediction. The predicted value is used as a new variable together with other variables in the XGBoost model for mining different attributes, and the predicted values of XGBoost are merged into the multivariate sequence, and then the new multidimensional data is converted. In order to supervise the learning sequence and use the LSTM model for prediction, the three model prediction results are weighted and combined, and the best combination weights are obtained through multiple experiments to calculate the final prediction value. The data results show that the multivariate prediction method based on the weighted combination of XGBoost and LSTM is more accurate than the prediction obtained by a single prediction method.