Abstract—This study compares the accuracy of ARIMA and Holt-Winters forecasting models in predicting China’s air cargo volume for a 5-year ahead horizon. Using time series data from 2000 to 2023. ARIMA model emphasizes autocorrelation within the data, while Holt-Winters model accounts for level and trend components, excluding seasonal effects. Both models are applied using univariate forecasting approaches to evaluate their performance in predicting future air cargo volumes. A comparison of the forecasting models for China's air cargo volume shows that the ARIMA (1,1,0) model outperforms the Holt-Winters non-seasonal smoothing model. ARIMA predicts a steady increase in cargo volume from 6.90 million tons in 2024 to 7.89 million tons in 2028, while Holt-Winters forecasts lower values, from 7.17 million tons in 2024 to 7.44 million tons in 2028. ARIMA performance indicator is also better, with lower RMSE, MAE, and MAPE, indicating more accuracy and reliability of predictions. This study highlights the ARIMA model's advantages in forecasting air cargo volume and provides valuable insights into different univariate forecasting methods.