基于SSA-SVR模型的国内新能源汽车销量预测研究

Research on Domestic New Energy Vehicle Sales Forecast Based on SSA-SVR Model

利用季节时间序列方法和机器学习算法,对新能源汽车销量展开精准预测研究。通过建立SARIMA模型和SSA-SVR模型,对2014年1月—2022年12月的国内新能源汽车月度销量数据进行建模预测,并对两模型进行拟合效果分析。实验结果表明,SSA-SVR模型具有更高的精度和泛化性能,更适合预测新能源汽车销量。使用SSA-SVR模型预测了2023年国内新能源汽车月度销量,并提出相关建议,以期促进新能源汽车实现可持续发展。

The seasonal time series method and machine learning algorithm are used to carry out accurate forecasting research on new energy vehicle sales.By establishing SARIMA model and SSA-SVR model,the monthly sales data of domestic new energy vehicles from January 2014 to December 2022 are modelled and forecasted,and the fitting effect of the two models is analysed.The experimental results show that the SSA-SVR model has higher accuracy and generalisation performance,which is more suitable for predicting the sales of new energy vehicles,and predicts the monthly sales of domestic new energy vehicles in 2023.Meanwhile,suggestions are made with a view to promoting new energy vehicles to achieve sustainable development.