Data characteristic analysis and model selection for container throughput forecasting within a decomposition-ensemble methodology

Abstract

In this study, a novel decomposition-ensemble methodology is proposed for container throughput forecasting. Firstly, the sample data of container throughput at ports are decomposed into several components. Secondly, the time series of the various components are thoroughly investigated to accurately capture the data characteristics. Then, an individual forecasting model is selected for each component based on the data characteristic analysis (DCA). Finally, the forecasting results are combined as an aggregated output. An empirical analysis is implemented for illustration and verification purposes. Our results suggest that proposed hybrid models can achieve better performance than other methods.