A novel secondary decomposition learning paradigm with kernel extreme learning machine for multi-step forecasting of container throughput

Abstract

Container throughput has been perceived as one of the most important factors of port economic development. Accurate forecasting of container throughput can not only improve the efficiency of container operation but also meet the requirements of financial trading. This paper proposes a secondary decomposition (SD) learning approach, which is integrated with a complementary ensemble empirical mode decomposition (CEEMD), sample entropy (SE), wavelet packet decomposition (WPD), extreme learning machine (ELM) and kernel extreme learning machine (KELM), to implement monthly container throughput forecasting. The CEEMD is conducted to decompose the original container data into several intrinsic mode function components and a residual term. The complexity of the subseries is measured by SE. Subsequently, the WPD is used to further smooth the most complex component. Then, ELM is executed to forecast the primary decomposition results, and KELM is employed to predict the secondary decomposition results. The final ensemble results can be obtained by integrating all of the forecasting results. The empirical results indicate that the SD learning approach is an excellent tool for forecasting nonlinear and nonstationary container throughput.