多变量乘用车销量预测模型研究

Research on Multivariable Prediction Model of Passenger Car Sales

在乘用车行业日渐繁荣的大环境下,准确把握行业发展方向,制定相适应的生产目标对各个车企来说非常重要。为了提高对乘用车销量的预测精度,选取历史销量、宏观经济指标和网络搜索关键词数据作为变量,对乘用车整体市场建立了多种销量预测模型,并经过对比分析得到综合考虑上述3种变量的梯度提升决策树模型效果最优,其平均绝对百分误差(MAPE)为10.35%,能够较好地预测销量变化。该模型可以帮助车企了解市场趋势,做出针对性的生产计划安排,同时为销量预测的研究提供一种新的参考模型。

In the environment that the passenger car industry is becoming increasingly prosperous,it is crucial for each automobile enterprise to accurately grasp the development direction of the industry and formulate suitable production goals.In this paper,historical sales,macroeconomic indicators and online search keyword data are selected as variables to establish a variety of sales prediction models for the overall passenger car market in order to improve the prediction accuracy of passenger car sales.Through comparative analysis,the Gradient Boosting Decision Tree(GDBT)algorithm model considering the above 3 variables has the best effect and its Mean Absolute Percentage Error(MAPE)is 10.35%.The model obtained in this paper can help automobile enterprises understand the development of market trends,make targeted production planning and provide a new reference model for the research of sales forecast.