Driving Mechanisms and Forecasting of China's Pet Population-An ARIMA-RF-HW Hybrid Approach

TL;DR

This paper introduces a hybrid ARIMA-RF-HW model that combines multiple forecasting techniques to accurately predict China's pet population, considering economic, social, and policy factors from 2005 to 2023.

Contribution

It presents a novel dynamically weighted hybrid model integrating ARIMA, Random Forest, and Holt-Winters methods for improved pet population forecasting in China.

Findings

Key drivers include urban income, consumption, and policy quantity.

Forecasts indicate steady growth for cats and fluctuating numbers for dogs.

The model enhances forecasting accuracy by capturing seasonality and nonlinear features.

Abstract

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.