多种算法融合的产品销售预测模型应用

销量预测一直是一个热点研究的课题，对于各个企业有着重要的意义.近年来，随着深度学习的崛起，用于销量预测的模型越来越多，而单一模型的预测性能往往不够理想，所以出现了越来越多的组合模型.本文利用Stacking策略将XGBoost、支持向量回归（Support Vector Regression，SVR）、GRU神经网络作为基础模型，然后将LightGBM作为最终的预测模型，并且融合了新的特征.集中了几种模型的优势，大大提高了模型的预测性能，更加接近真实的销量数据，为回归预测提供一种新的预测方法.

Application of Product Sales Forecast Model Based on Multiple Algorithm Fusion

Sales forecasting has always been a hot research topic and has great significance for all enterprises. In recent years, with the rise of deep learning, there are more and more models for sales forecasting, and the performance of single models is often not ideal. Therefore, there are more and more combinatorial models. In this study, we use Stacking strategy to support XGBoost, Support Vector Regression (SVR), GRU neural network as the basic model, then lightGBM as the final prediction model, with new features are merged. The advantages of several models are condensed, which greatly improves the prediction performance of the model, good enough to be more close to the real sales data, and provide a new prediction method for regression prediction.