IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i13p2234-d848077.html
   My bibliography  Save this article

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

Author

Listed:
  • Fei Qu

    (School of Business, Guilin University of Technology, Guilin 541004, China)

  • Yi-Ting Wang

    (School of Business, Central South University, Changsha 410083, China)

  • Wen-Hui Hou

    (School of Business, Central South University, Changsha 410083, China)

  • Xiao-Yu Zhou

    (School of Business, Central South University, Changsha 410083, China)

  • Xiao-Kang Wang

    (School of Business, Central South University, Changsha 410083, China)

  • Jun-Bo Li

    (School of Business, Guilin University of Technology, Guilin 541004, China)

  • Jian-Qiang Wang

    (School of Business, Central South University, Changsha 410083, China)

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.

Suggested Citation

  • Fei Qu & Yi-Ting Wang & Wen-Hui Hou & Xiao-Yu Zhou & Xiao-Kang Wang & Jun-Bo Li & Jian-Qiang Wang, 2022. "Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm," Mathematics, MDPI, vol. 10(13), pages 1-22, June.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2234-:d:848077
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/13/2234/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/13/2234/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Aycan Kaya & Gizem Kaya & Ferhan Çebi, 2019. "Forecasting Automobile Sales in Turkey with Artificial Neural Networks," International Journal of Business Analytics (IJBAN), IGI Global, vol. 6(4), pages 50-60, October.
    2. Sa-ngasoongsong, Akkarapol & Bukkapatnam, Satish T.S. & Kim, Jaebeom & Iyer, Parameshwaran S. & Suresh, R.P., 2012. "Multi-step sales forecasting in automotive industry based on structural relationship identification," International Journal of Production Economics, Elsevier, vol. 140(2), pages 875-887.
    3. Jan R. Landwehr & Aparna A. Labroo & Andreas Herrmann, 2011. "Gut Liking for the Ordinary: Incorporating Design Fluency Improves Automobile Sales Forecasts," Marketing Science, INFORMS, vol. 30(3), pages 416-429, 05-06.
    4. Kostyra, Daniel S. & Reiner, Jochen & Natter, Martin & Klapper, Daniel, 2016. "Decomposing the effects of online customer reviews on brand, price, and product attributes," International Journal of Research in Marketing, Elsevier, vol. 33(1), pages 11-26.
    5. Moussa, Faten & Delhoumi, Ezzeddine & Ouda, Olfa Ben, 2017. "Stock return and volatility reactions to information demand and supply," Research in International Business and Finance, Elsevier, vol. 39(PA), pages 54-67.
    6. Liu, Keyan & Zhou, Jianan & Dong, Dayong, 2021. "Improving stock price prediction using the long short-term memory model combined with online social networks," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    7. Fan, Zhi-Ping & Che, Yu-Jie & Chen, Zhen-Yu, 2017. "Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis," Journal of Business Research, Elsevier, vol. 74(C), pages 90-100.
    8. Peng, Yanni & Xiang, Wanli, 2020. "Short-term traffic volume prediction using GA-BP based on wavelet denoising and phase space reconstruction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Ning & Shang, Kai & Duan, Yan & Qin, Dandan, 2023. "Carbon quota allocation modeling framework in the automotive industry based on repeated game theory: A case study of ten Chinese automotive enterprises," Energy, Elsevier, vol. 279(C).
    2. Tendai Makoni & Delson Chikobvu, 2023. "Assessing and Forecasting the Long-Term Impact of the Global Financial Crisis on New Car Sales in South Africa," Data, MDPI, vol. 8(5), pages 1-16, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ruixin Ding & Bowei Chen & James M. Wilson & Zhi Yan & Yufei Huang, 2023. "SRNI-CAR: A comprehensive dataset for analyzing the Chinese automotive market," Papers 2401.05395, arXiv.org.
    2. Agnieszka Zablocki & Bodo Schlegelmilch & Michael J. Houston, 2019. "How valence, volume and variance of online reviews influence brand attitudes," AMS Review, Springer;Academy of Marketing Science, vol. 9(1), pages 61-77, June.
    3. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
    4. Konstantakis, Konstantinos N. & Milioti, Christina & Michaelides, Panayotis G., 2017. "Modeling the dynamic response of automobile sales in troubled times: A real-time Vector Autoregressive analysis with causality testing for Greece," Transport Policy, Elsevier, vol. 59(C), pages 75-81.
    5. Sohrabpour, Vahid & Oghazi, Pejvak & Toorajipour, Reza & Nazarpour, Ali, 2021. "Export sales forecasting using artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    6. Bitty Balducci & Detelina Marinova, 2018. "Unstructured data in marketing," Journal of the Academy of Marketing Science, Springer, vol. 46(4), pages 557-590, July.
    7. (Kay) Byun, Kyung-ah & Ma, Minghui & Kim, Kevin & Kang, Taeghyun, 2021. "Buying a New Product with Inconsistent Product Reviews from Multiple Sources: The Role of Information Diagnosticity and Advertising," Journal of Interactive Marketing, Elsevier, vol. 55(C), pages 81-103.
    8. George Doorn & Bryan Paton & Charles Spence, 2016. "Is J the new K? Initial letters and brand names," Journal of Brand Management, Palgrave Macmillan, vol. 23(6), pages 666-678, November.
    9. Zhang, Tianyu & Dong, Peiwu & Zeng, Yongchao & Ju, Yanbing, 2022. "Analyzing the diffusion of competitive smart wearable devices: An agent-based multi-dimensional relative agreement model," Journal of Business Research, Elsevier, vol. 139(C), pages 90-105.
    10. Bag, Sujoy & Tiwari, Manoj Kumar & Chan, Felix T.S., 2019. "Predicting the consumer's purchase intention of durable goods: An attribute-level analysis," Journal of Business Research, Elsevier, vol. 94(C), pages 408-419.
    11. Bi-Huei Tsai & Yao-Min Huang, 2023. "Comparing the Substitution of Nuclear Energy or Renewable Energy for Fossil Fuels between the United States and Africa," Sustainability, MDPI, vol. 15(13), pages 1-16, June.
    12. Minnema, Alec & Bijmolt, Tammo H.A. & Gensler, Sonja & Wiesel, Thorsten, 2016. "To Keep or Not to Keep: Effects of Online Customer Reviews on Product Returns," Journal of Retailing, Elsevier, vol. 92(3), pages 253-267.
    13. Xu, Mingli & Yang, Wei & Huang, Zhixiong, 2021. "Do investor relations matter in the tourism industry? Evidence from public opinions in China," Economic Modelling, Elsevier, vol. 94(C), pages 923-933.
    14. Doris Chenguang Wu & Shiteng Zhong & Richard T R Qiu & Ji Wu, 2022. "Are customer reviews just reviews? Hotel forecasting using sentiment analysis," Tourism Economics, , vol. 28(3), pages 795-816, May.
    15. Sharma, Dheeraj & Pandey, Shivendra, 2020. "The role payment depreciation in short temporal separations: Should online retailer make customers wait?," Journal of Retailing and Consumer Services, Elsevier, vol. 53(C).
    16. Zhe OuYang & Peng Cheng & Yang Liu, 2020. "The role of product line breadth, product pre-entry experience, and market uncertainty in explaining followers’ speed of feature entry," Review of Managerial Science, Springer, vol. 14(6), pages 1221-1249, December.
    17. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    18. Dörnyei, Krisztina Rita & Lunardo, Renaud, 2021. "When limited edition packages backfire: The role of emotional value, typicality and need for uniqueness," Journal of Business Research, Elsevier, vol. 137(C), pages 233-243.
    19. Johannes Habel & Sascha Alavi & Nicolas Heinitz, 2023. "A theory of predictive sales analytics adoption," AMS Review, Springer;Academy of Marketing Science, vol. 13(1), pages 34-54, June.
    20. Ketron, Seth, 2017. "Investigating the effect of quality of grammar and mechanics (QGAM) in online reviews: The mediating role of reviewer crediblity," Journal of Business Research, Elsevier, vol. 81(C), pages 51-59.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:13:p:2234-:d:848077. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.