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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.11269 |
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| _version_ | 1866912379862777856 |
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| author | Chang, Shengjia Yue, Xianshuo |
| author_facet | Chang, Shengjia Yue, Xianshuo |
| contents | This study proposes a dynamically weighted ARIMA-RF-HW hybrid model integrating ARIMA for seasonality and trends, Random Forest for nonlinear features, and Holt-Winters smoothing for seasonal adjustment to improve China's pet population forecasting accuracy. Using 2005-2023 data with nine economic, social, and policy indicators (urban income, consumption, aging ratio, policy quantity, new veterinary drug approvals), data were preprocessed via Z-score normalization and missing value imputation. The results show that key drivers of pet populations include urban income (19.48% for cats, 17.15% for dogs), consumption (17.99% for cats), and policy quantity (13.33% for cats, 14.02% for dogs), with aging (12.81% for cats, 13.27% for dogs) and urbanization amplifying the demand for pets. Forecasts show steady cat growth and fluctuating dog numbers, reflecting cats' adaptability to urban environments. This research supports policymakers in optimizing pet health management and guides enterprises in developing differentiated services, advancing sustainable industry growth. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_11269 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Driving Mechanisms and Forecasting of China's Pet Population-An ARIMA-RF-HW Hybrid Approach Chang, Shengjia Yue, Xianshuo Machine Learning This study proposes a dynamically weighted ARIMA-RF-HW hybrid model integrating ARIMA for seasonality and trends, Random Forest for nonlinear features, and Holt-Winters smoothing for seasonal adjustment to improve China's pet population forecasting accuracy. Using 2005-2023 data with nine economic, social, and policy indicators (urban income, consumption, aging ratio, policy quantity, new veterinary drug approvals), data were preprocessed via Z-score normalization and missing value imputation. The results show that key drivers of pet populations include urban income (19.48% for cats, 17.15% for dogs), consumption (17.99% for cats), and policy quantity (13.33% for cats, 14.02% for dogs), with aging (12.81% for cats, 13.27% for dogs) and urbanization amplifying the demand for pets. Forecasts show steady cat growth and fluctuating dog numbers, reflecting cats' adaptability to urban environments. This research supports policymakers in optimizing pet health management and guides enterprises in developing differentiated services, advancing sustainable industry growth. |
| title | Driving Mechanisms and Forecasting of China's Pet Population-An ARIMA-RF-HW Hybrid Approach |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.11269 |