An Analysis and Forecasting of Air Cargo Volume in China Under the Impacts of COVID-19 Epidemic

With the impacts of COVID-19 epidemic on air cargo market, monthly air cargo volumedata in China shows extreme values, whichare inconsistent with historical trends. As traditional forecasting modelsof air cargo volume are susceptible to large errors due to extreme data, several short-term forecastingmethodsare proposed and developed to forecast air cargo volume in the post-epidemic era of China. It is found thatair cargo volume in China under the influence of COVID-19 epidemic has a steady growth upward trend along with a significant, short-term fluctuation after analyzing the monthly data of air cargo volume in China from 2009 to 2020. Assuming the impactsof COVID-19 epidemic on air cargo decrease gradually, Holt-Winters multiplication model and autoregressive integrating moving average (ARIMA) multiplication seasonal model are applied to model the long-term trend, periodic characteristic, and short-term fluctuation of air cargo quantities respectively. In addition, four different methods for selecting the weights are applied to these two models, in order todevelop combined forecasting models of air cargo volume. Holt-Winter model, ARIMA model, and the combined forecasting model based on the two techniques are used to forecast monthly domestic air cargo volume from 2021 to 2022. The forecasting errors of these models are compared and analyzed based on domestic air cargo volume data from January to May in 2021. The results show that the average absolute percentage error (AAPE) and the maximum absolute percentage error (MAPE) of the Holt-Winters and ARIMA combined model are generally smaller than those of any single model. The combined model weighted by the least square method is found to be most accurate, while itthat based on weights determined by residual reciprocal method is ranked second. The AAPEof the combined model is 1.93%, which is reduced by 8.53% whencompared with the combined model ranked second, and is 71.70% and 20.58% lower than that of single Holt-Winters and ARIMA model. Therefore, the effectiveness and accuracy of the optimized, combined model in forecasting the monthly domestic air cargo volume within the post-epidemic era has been verified.