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

Author

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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)

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.

Suggested Citation

  • Mingyang Zhang & Heyan Xu & Ning Ma & Xinglin Pan, 2022. "Intelligent Vehicle Sales Prediction Based on Online Public Opinion and Online Search Index," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:16:p:10344-:d:892697
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    Cited by:

    1. Shaobo Liang & Dan Wu & Jing Dong, 2022. "Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches," Sustainability, MDPI, vol. 14(20), pages 1-19, October.
    2. Min Zhao & Yu Fang & Debao Dai, 2023. "Forecast of the Evolution Trend of Total Vehicle Sales and Power Structure of China under Different Scenarios," Sustainability, MDPI, vol. 15(5), pages 1-22, February.

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