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24 May 2022 Research on automobile sales prediction model based on BP neural network
Yan Zhou, Jun Zhou
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Proceedings Volume 12260, International Conference on Computer Application and Information Security (ICCAIS 2021); 122601J (2022) https://doi.org/10.1117/12.2637682
Event: International Conference on Computer Application and Information Security (ICCAIS 2021), 2021, Wuhan, China
Abstract
This paper established a theory framework of the correlation between consumers’ web information search and related products’ sales. Taking the Chinese customers’ search behavior using Baidu search engine and the search data left during the decision-making process, this paper built up and filtered a search keyword thesaurus with high correlation of automobile sales and leading time difference. Then a brand automobile monthly sales prediction model was set up based on BP Neutral Network. On this basis, this paper took a specific automobile model for example and predicted its monthly sales for a month. The predicted results showed that the absolute average percentage error was 5.6%, which was 0.5% lower than the MAPE model by improved principal component analysis, and the prediction accuracy was improved. The validity and rationality of the model were verified. This paper provides a new idea for product sales forecasting of automobile enterprises, and also can be used as a reference for other industries.
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Yan Zhou and Jun Zhou "Research on automobile sales prediction model based on BP neural network", Proc. SPIE 12260, International Conference on Computer Application and Information Security (ICCAIS 2021), 122601J (24 May 2022); https://doi.org/10.1117/12.2637682
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KEYWORDS
Data modeling
Neural networks
Neurons
Principal component analysis
Internet
Error analysis
Analytical research
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