A new secondary decomposition-ensemble approach with cuckoo search optimization for air cargo forecasting

• Published in: Applied soft computing Vol. 90; p. 106161
• Main Authors: Li, Hongtao, Bai, Juncheng, Cui, Xiang, Li, Yongwu, Sun, Shaolong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2020
• Subjects: Air cargo forecasting; Cuckoo search algorithm; Elman neural networks; Phase space reconstruction; Secondary decomposition-ensemble learning; Cuckoo search algorithm; Elman neural networks; Secondary decomposition-ensemble learning; Phase space reconstruction; Air cargo forecasting
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2020.106161

The accurate forecast of air cargo demand is essential for infrastructure construction planning and daily operation management. Evidently, it is extremely difficult to capture the dynamics of time series impacted by distinct sources. To reduce the complexity of data, the current popular method is to decompose the original data into several modal branches with different characteristic attributes. But the new problem is that the components generated by decomposition are still irregular and unstable, and there is no unified method to predict them. In this paper, a new secondary decomposition-ensemble (SDE) approach with a cuckoo search algorithm (CSA) is proposed for air cargo forecasting. More specifically, the original air cargo time series is decomposed into several components by an enhanced decomposition formwork, which consists of variational mode decomposition (VMD), sample entropy (SE) and empirical mode decomposition (EMD). Subsequently, the ARIMA and the Elman neural networks (ENN) optimized by CSA are respectively applied to forecast the trend component and the low-frequency components, during which the phase space reconstruction (PSR) is conducted to determine the input structure of neural networks. The final forecasting results are obtained by integrating the predicted values of each component. Besides, the air cargo series from three different airports in China are adopted to validate the performance of our proposed approach and the empirical results show that it is superior to all other benchmark models in terms of the robustness and accuracy. •A new enhanced secondary decomposition-ensemble approach is proposed for air cargo forecasting.•The phase space reconstruction is employed to design the form of Elman neural network.•The cuckoo search algorithm is used to improve the forecasting ability of Elman neural network.•The ARIMA model is applied to predict the trend component of modes.•Empirical results verify that the proposed approach outperforms other considered benchmark models.