Ma Xiaoya, Li Mengxiu, Tong Jin, Feng Xiaying
Department of Logistics Management and Engineering, Nanning Normal University, Nanninng 530023, China.
Department of Economics and Management, Nanning Normal University, Nanninng 530001, China.
Biomimetics (Basel). 2023 Jul 15;8(3):312. doi: 10.3390/biomimetics8030312.
Low-carbon and environmentally friendly living boosted the market demand for new energy vehicles and promoted the development of the new energy vehicle industry. Accurate demand forecasting can provide an important decision-making basis for new energy vehicle enterprises, which is beneficial to the development of new energy vehicles. From the perspective of an intelligent supply chain, this study explored the demand forecasting of new energy vehicles, and proposed an innovative SARIMA-LSTM-BP combination model for prediction modeling. The data showed that the RMSE, MSE, and MAE values of the SARIMA-LSTM-BP combination model were 2.757, 7.603, and, 1.912, respectively, all of which are lower values than those of the single models. This study therefore, indicated that, compared with traditional econometric forecasting models and deep learning forecasting models, such as the random forest, support vector regression (SVR), long short-term memory (LSTM), and back propagation neural network (BP) models, the SARIMA-LSTM-BP combination model performed outstandingly with higher accuracy and better forecasting performance.
低碳环保生活提升了新能源汽车的市场需求，推动了新能源汽车产业的发展。准确的需求预测可为新能源汽车企业提供重要的决策依据，有利于新能源汽车的发展。从智能供应链的角度出发，本研究探讨了新能源汽车的需求预测，并提出了一种创新的SARIMA-LSTM-BP组合模型用于预测建模。数据显示，SARIMA-LSTM-BP组合模型的RMSE、MSE和MAE值分别为2.757、7.603和1.912，均低于单一模型的值。因此，本研究表明，与传统计量经济预测模型和深度学习预测模型（如随机森林、支持向量回归（SVR）、长短期记忆（LSTM）和反向传播神经网络（BP）模型）相比，SARIMA-LSTM-BP组合模型表现出色，具有更高的准确性和更好的预测性能。
