Study on the electric vehicle sales forecast with TEI@I methodology. Issue 1 (9th December 2021)

• Record Type: Journal Article
• Authors: Wan, Jiang Ping; Xie, Le Qi; Hu, Xue Fang
• Is Part Of: International journal of knowledge engineering and data mining. Volume 7:Issue 1/2(2020)
• Page Start: 1
• Page End: 38
• Publication Date: 2021-12-09
• Subjects: electric vehicle -- forecast model -- CiteSpace -- TEI@I methodology -- principal component regression analysis -- BP neural network -- support vector machine -- SVM -- Baidu search index; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

Abstract

Electric vehicle (EV) sales are affected in many ways (especially in China), and there are few available sales forecasting models. The research was a decomposition and integration based on TEI@I methodology: the prediction model applied the principal component regression (PCR) analysis to deal with the linear relationship; then applied BP neural network and a support vector machine (SVM) to deal with the nonlinear relationship; and finally, they are all integrated together. Granger causality test and grey correlation degree are used to quantitatively analyse the factors affecting the sales of electric vehicles through mining consumer network data. The research results of EV models show that the Baidu search index lags behind for three months and is time-sensitive to the EV sales. Finally, taking the data of two car models as examples, it is found that the PCR-BP model and the PCR-SVM model have better prediction performance than the single model. It also provides an effective decision-making reference for similar product market prediction.