Analysis and combined forecasting of China containerized freight index based on VMD
Systems Engineering - Theory & Practice
2021, 41 (1):
Following the idea of decomposition-reconstruction-subsequence forecasting-ensemble, a combined forecasting model based on variational mode decomposition (VMD) was proposed. The model was constructed by selecting suitable decomposition model, optimizing reconstruction method, choosing appropriate subsequence forecasting method and ensemble method. And it was used to forecast the China containerized freight index (CCFI) and analyze the volatility characteristics and economic connotations of CCFI. Firstly, The time series CCFI was decomposed into multiple modal components by using VMD. Secondly, The modal components were reconstructed into high frequency, medium frequency, low frequency and trend subsequences, which means short-term market imbalance factors, seasonal factors, major events and market supply and demand respectively. Here, the fuzzy C-clustering algorithm was used to reconstruct the modal components, and its parameter C was optimized by component time-scale analysis. The economic meaning of each subsequence was explored by analyzing its volatility characteristics. Thirdly, a method based on data feature analysis was proposed to select the proper forecasting models, and it was used for reconstruct subsequences forecast. Finally, forecast results of reconstructed subsequences were added to obtain final output, and the ensemble forecast output was compared with other models' forecast results. The empirical results showed that the combined forecast model proposed in this paper is superior to the single model, such as BPNN, SVM, ARIMA, and EMD combination model, as well as other multi-scale combined forecast models based on VMD. And the analysis results reflected the external fluctuation characteristics and intrinsic economic meaning of CCFI.
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