A new energy vehicle sales forecasting model based on high-dimensional tensor CNN

Xinyi Ye; Wei Le

doi:10.1117/12.2671046

28 April 2023 A new energy vehicle sales forecasting model based on high-dimensional tensor CNN

Xinyi Ye, Wei Le

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 1261062 (2023) https://doi.org/10.1117/12.2671046
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Traditional forecasting models mainly utilize historical sales and a few macroeconomic indicators, can no longer reflect the impact of new energy vehicle sales. Based on high-dimensional tensor CNN, this paper proposes the new-energy vehicle sales prediction model and explores the prediction effect of high-dimensional data and deep learning on new energy vehicle sales, the factors that affect sales is divided into consumer dimension, vehicle dimension and social dimension. In each dimension, we choose 25 high comprehensive influence factors, then integrate them into a tensor structure. Through the one-dimensional multi-kernel convolutional neural network, the correlation between sales and tensor data is learnt. Furthermore, thirty vehicle brands with sales advantages in recent three years are verified. The experimental results show that, compared with linear regression, random forest, light gradient boosting machine and convolutional neural network, that commonly used in sales forecasting.

Citation Download Citation

Xinyi Ye and Wei Le "A new energy vehicle sales forecasting model based on high-dimensional tensor CNN", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 1261062 (28 April 2023); https://doi.org/10.1117/12.2671046

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