Research on Predicting China's Wildlife Conservation Index Based on ARIMA Model

In recent years, time series analysis has emerged as a vital tool for understanding complex trends and patterns in various fields, including wildlife conservation. Given the dynamic nature of ecological data, developing models capable of capturing both short-term fluctuations and long-term trends is essential for reliable forecasting. This study focuses on predicting the China Wildlife Conservation Society Index through a structured approach to time series modeling. After data collection and preprocessing steps—such as removing seasonality and trends—modeling began with assessments of stationarity using the Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests. A first-order differencing operation was applied to make the series stationary, followed by model order selection based on AIC and BIC criteria. An optimal ARIMA model was then established and fitted, with residual diagnostics confirming model adequacy. Using this ARIMA model, predictions were made for the subsequent five-time points, with a calculated 95% confidence interval. The prediction results, well-aligned with the confidence interval, demonstrate the model's reliability. This approach supports strategic decision-making in wildlife conservation by offering accurate forecasting capabilities for key ecological indicators.