Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index

Mingyang Zhang (), Heyan Xu, Ning Ma and Xinglin Pan
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Mingyang Zhang: School of Economics and Management, Beijing Forestry University, Beijing 100083, China
Heyan Xu: School of Economics and Management, Beijing Forestry University, Beijing 100083, China
Ning Ma: School of Economics and Management, Beijing Forestry University, Beijing 100083, China
Xinglin Pan: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China

Sustainability, 2022, vol. 14, issue 16, 1-17

Abstract: Intelligent vehicles refer to a new generation of vehicles with automatic driving functions that is gradually becoming an intelligent mobile space and application terminal by carrying advanced sensors and other devices and using new technologies, such as artificial intelligence. Firstly, the traditional autoregressive intelligent vehicle sales prediction model based on historical sales is established. Secondly, the public opinion data and online search index data are selected to establish a sales prediction model based on online public opinion and online search index. Then, we consider the influence of KOL (Key Opinion Leader), a sales prediction model based on KOL online public opinion andonline search index is established. Finally, the model is further optimized by using the deep learning algorithm LSTM (Long Short-Term Memory network), and the LSTM sales prediction model based on KOL online public opinion and online search index is established. The results show that the consideration of the online public opinion and search index can improve the prediction accuracy of intelligent vehicle sales, and the public opinion of KOL plays a greater role in improving the prediction accuracy of sales than that of the general public. Deep learning algorithms can further improve the prediction accuracy of intelligent vehicle sales.

Keywords: sales prediction; intelligent vehicle; sentiment analysis; regression analysis; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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