A Combination Forecast Method of Port Container Throughput Based on Secondary Decomposition and Model Selection Strategy

Abstract

Abstract: Accurate prediction of port container throughput is of great significance to government planning of port construction and reasonable allocation of resources by ports and shipping enterprises. Previous studies often use a single decomposition method to deal with the complex features in the container throughput series, which results in incomplete data feature extraction and blind selection of prediction models, and greatly affects the prediction effect of the combined model. Therefore, this paper introduces the strategies of secondary decomposition and model selection based on data characteristics analysis to predict the port container throughput by establishing a combination forecast framework. Firstly, according to the overall characteristics of the original series, a decomposition method is selected to decompose it preliminarily, and several components are obtained. Then, the data characteristics of each component, such as stationarity, seasonality and complexity, are analyzed, and an appropriate econometric model is selected for prediction or the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the component. Next, the characteristic of long-range correlation is introduced, and the appropriate model for prediction is selected according to the stationarity, complexity, and long-range correlation of the sub-sequences after the secondary decomposition. Finally, the predicted values of all components are integrated to obtain the final prediction results. Taking monthly forecast as an example, the empirical study uses the container throughput data of Shanghai Port and Tianjin Port as samples. The empirical results show that the combination forecast framework proposed in this paper has higher prediction accuracy than the benchmark models, and is a promising tool for port container throughput forecasting, which can provide decision-making reference for relevant government departments, ports, and shipping enterprises.

Key words: forecast of container throughput, secondary decomposition, data characteristics analysis, model selection, combination forecast

Liang Xiaozhen, Zhao Xin, Yang Mingge, Wu Junfeng, Deng Tianhu, Tian Xin. A Combination Forecast Method of Port Container Throughput Based on Secondary Decomposition and Model Selection Strategy[J]. Management Review, 2024, 36(8): 52-64.

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