Title: Study on the electric vehicle sales forecast with TEI@I methodology

Authors: Jiang Ping Wan; Le Qi Xie; Xue Fang Hu

Addresses: Faculty of Business Administration, South China University of Technology, Wushan Guangzhou, Guangdong, China ' Faculty of Business Administration, South China University of Technology, Wushan Guangzhou, Guangdong, China ' Faculty of Business Administration, South China University of Technology, Wushan Guangzhou, Guangdong, China

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.

Keywords: electric vehicle; forecast model; CiteSpace; TEI@I methodology; principal component regression analysis; BP neural network; support vector machine; SVM; Baidu search index.

DOI: 10.1504/IJKEDM.2021.119836

International Journal of Knowledge Engineering and Data Mining, 2021 Vol.7 No.1/2, pp.1 - 38

Received: 10 Dec 2019
Accepted: 25 Feb 2020
Published online: 22 Dec 2021 *

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