Research on the Chinese Pet Market Based on Multivariate Prediction Model

Authors

  • Wenbaiyang Wu

DOI:

https://doi.org/10.54097/5jqym959

Keywords:

Grey Model, ARIMA, Multiple Linear Regression, Pet Market.

Abstract

China's pet industry is experiencing robust growth, prompting this research to develop a series of innovative forecasting models that not only predict the future number of pets and the size of the pet food market in China and globally but also offer practical suggestions for industry expansion. A key innovation lies in the integration of the Grey Model GM(1,1) to forecast the future population size of pets in China with high precision, coupled with correlation analysis to pinpoint the critical factors driving industry development. Furthermore, the study employs the ARIMA model to predict global pet food demand trends and estimates China's pet food production, demonstrating a holistic approach. An additional novelty is the use of a multiple linear regression model to forecast China's pet food export volume, considering various domestic and international factors, thereby providing a comprehensive understanding and prediction of the pet industry's future trajectory.

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References

[1] Kwak, M. K., Cha, S. S. A study on the selection attributes affecting pet food purchase: After COVID-19 pandemic[J]. International Journal of Food Properties, 2021, 24(1): 291-303.

[2] Liu, Z. Unlocking consumer choices in the digital economy: Exploring factors influencing online and offline purchases in the emerging pet food market[J]. Journal of the Knowledge Economy, 2023, 15(3): 10174-10199.

[3] Bi, Z., Du, Y., Hao, R. Predict the price change over time based on GM (1,1) model[J]. Financial Engineering and Risk Management, 2022, 5(2).

[4] Li, Y. S., Zhang, L. G., Song, T. A study on forecasting the popularity of Chinese national brands using a grey-weighted Markov model[J]. Electronic Commerce Research, 2024, [prepublish]: 1-17.

[5] Yu, H., Hutson, A. D. Inferential procedures based on the weighted Pearson correlation coefficient test statistic[J]. Journal of Applied Statistics, 2024, 51(3): 481-496.

[6] Liu, P., Wang, S., Zhao, P. Robust estimation and test for Pearson’s correlation coefficient[J]. Random Matrices: Theory and Applications, 2024.

[7] Kushnir, M., Tokarieva, K. A generalization of the ARIMA model to the nonlinear and continuous cases[J]. Cybernetics and Systems Analysis, 2023, 59(6): 900-909.

[8] Li, X., Liu, X., Zhong, Y. Research on sales and pricing optimization of vegetable category based on ARIMA model and exponential smoothing method[J]. Information Systems and Economics, 2024, 5(3).

[9] Ondieki, R., Waititu, H., Nyabwanga, R. N. Modeling and forecasting high frequency currency exchange rates: A comparative study of ARIMA, ANN, and hybrid ARIMA-ANN models[J]. Asian Journal of Probability and Statistics, 2024, 26(10): 32-45.

[10] Quang, P. D., Oanh, N. T., Hao, L. T. P., Duong, P. H., Linh, L. K., Ngan, N. T. K. Estimating and forecasting bitcoin daily prices using ARIMA-GARCH models[J]. Business Analyst Journal, 2024, 45(1): 11-23.

[11] Yan, Z. Real estate price model based on multiple linear regression and analysis of influencing factors of regional economic vitality development[J]. Academic Journal of Business & Management, 2021, 3(4).

[12] Peijiang, C., Xueyin, Y. Study on the evaluation method of blended learning effect based on multiple linear regression analysis[J]. International Journal of Web-Based Learning and Teaching Technologies (IJWLTT), 2023, 18(1): 1-15.

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Published

25-03-2025

How to Cite

Wu, W. (2025). Research on the Chinese Pet Market Based on Multivariate Prediction Model. Highlights in Science, Engineering and Technology, 131, 158-164. https://doi.org/10.54097/5jqym959