Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm

Fei Qu · Yi-ting Wang · Wen-hui Hou · Xiaoyu Zhou · Xiaokang Wang · Junbo Li +1 more

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Forecasting Techniques and Applications Data Mining and Machine Learning Applications Grey System Theory Applications

Abstract

With the development of the Internet and big data, more and more consumer behavior data are used in different forecasting problems, which greatly improve the performance of prediction. As the main travel tool, the sales of automobiles will change with the variations of the market and the external environment. Accurate prediction of automobile sales can not only help the dealers adjust their marketing plans dynamically but can also help the economy and the transportation sector make policy decisions. The automobile is a product with high value and high involvement, and its purchase decision can be affected by its own attributes, economy, policy and other factors. Furthermore, the sample data have the characteristics of various sources, great complexity and large volatility. Therefore, this paper uses the Support Vector Regression (SVR) model, which has global optimization, a simple structure, and strong generalization abilities and is suitable for multi-dimensional, small sample data to predict the monthly sales of automobiles. In addition, the parameters are optimized by the Grey Wolf Optimizer (GWO) algorithm to improve the prediction accuracy. First, the grey correlation analysis method is used to analyze and determine the factors that affect automobile sales. Second, it is used to build the GWO-SVR automobile sales prediction model. Third, the experimental analysis is carried out by using the data from Suteng and Kaluola in the Chinese car segment, and the proposed model is compared with the other four commonly used methods. The results show that the GWO-SVR model has the best performance of mean absolute percentage error (MAPE) and root mean square error (RMSE). Finally, some management implications are put forward for reference.

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2020Stock Closing Price Prediction using Machine Learning Techniques 2020Incorporating stock prices and news sentiments for stock market prediction: A case of Hong Kong 2017Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis 2018The effect of online reviews on product sales: A joint sentiment-topic analysis 2020Mean–variance portfolio optimization using machine learning-based stock price prediction 2015Decomposing the effects of online customer reviews on brand, price, and product attributes 2021A network traffic forecasting method based on SA optimized ARIMA–BP neural network 2011Gut Liking for the Ordinary: Incorporating Design Fluency Improves Automobile Sales Forecasts

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