Demand forecasting of transportation service network of food cold chain based on a combined model of trend double exponential smoothing and improved grey methods

In a competitive market, the accurate forecasting of short-term transport demand is critical to the transportation service network of a food cold chain. In this paper a model that combines trend double exponential smoothing and improved grey forecasting methods is proposed to predict the short-term cold chain transport demand of transportation service networks of a food cold chain, showing changes in trends and seasonal fluctuations having irregular periods. The combined model is constructed to fit the changing trends and the featured seasonal fluctuation periods. In order to improve forecasting accuracy and model adaptability, the combined model is modelled repeatedly to fit the remnant tail time series of the main combination model until forecast accuracy is achieved. The modelling approach is applied to the freight companies engaged in the transportation of the food cold chain in China. The results demonstrate that the proposed modelling approach produces acceptable forecasting results and goodness of fit, also showing good model adaptability in an uncertain environment. This fact makes the modelling approach an option for predicting the short-term transportation demands of the food cold chain transportation service network.