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| Forecasting Car Sales Based on Consumer Attention |
Jiang Cuiqing1,2,Wang Xiangxiang1( ),Wang Zhao1 |
1School of Management, Hefei University of Technology, Hefei 230009, China
2Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei 230009, China |
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Abstract [Objective] This study constructs a forecasting model for car sales based on consumer attention. [Methods] First, we defined consumer attention with consumer opinion and search data. Then, we used the Word2Vec algorithm to extract the initial keyword lists, while using time difference correlation analysis to identify the core keywords. Finally, we generated the user attention data with PCA and built Attention_LSTM model to predict car sales. [Results] The RMSE and MAPE indices of our model were reduced by 2.02 and 0.96%. The average percentage error of the new model was 6.52%, 3.42%, 2.56%, and 0.81% less than those of the ARIMA, SVR, BP neural network, and LSTM models. [Limitations] We did not include other social media data to analyze consumers’ online behaviors. [Conclusions] The Attention_LSTM model based on consumer attention could effectively forecast auto sales.
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Received: 12 May 2020
Published: 05 February 2021
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| Fund:The work is supported by the National Natural Science Foundation of China Grant No(71731005);the Humanities and Social Sciences Planning Fund of the Ministry of Education Grant No(15YJA630010) |
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Corresponding Authors:
Wang Xiangxiang
E-mail: 2285237002@qq.com
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