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A Real-Time Forecast Model Based on Convolutional Neural Network and Attention Mechanism for Passenger Car Sales in 5G Environment
Published in IEEE Transactions on Intelligent Transportation Systems, 2023
Achieving accurate forecasts of passenger car sales can help car companies set reasonable sales targets. However, the existing forecast models are plagued by the following problems. First, the models do not take into account the impact of the importance of features on the forecast ability. Secondly, single feature data cannot reflect the complex buying and selling logic of the passenger car market, and the previous models have not been able to explore the combination effects between different features well. Therefore, in this work, we propose a passenger car sales forecast model based on the convolutional neural network and attention mechanism (PCSFCA). Its innovation lies firstly in the use of the attention mechanism to calculate the importance of features, which enables the model to value important features and ignore unimportant ones. The second is the use of convolutional neural networks to extract the higher-order information of features, which facilitates the model to capture complex data distribution. Besides, we use the 5G network to build a cloud platform for real-time collection of passenger car sales records. The collected sales data are input to the model, and then the model can be learned in real-time. The comparison of experimental results with several benchmark models illustrates the effectiveness of the PCSFCA model.
Recommended citation: Y. Lu, Z. Shu, A. Li and H. Zhang, "A Real-Time Forecast Model Based on Convolutional Neural Network and Attention Mechanism for Passenger Car Sales in 5G Environment," in IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 3, pp. 2858-2868, March 2024, doi: 10.1109/TITS.2023.3311541.
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