A deep learning-based multivariate decomposition and ensemble framework for container throughput forecasting

IF 3.4 3区 经济学 Q1 ECONOMICS Journal of Forecasting Pub Date : 2024-05-14 DOI:10.1002/for.3151
Anurag Kulshrestha, Abhishek Yadav, Himanshu Sharma, Shikha Suman
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Abstract

Traditional linear models struggle to capture the intricate relationship between dynamic container throughput and its complex interplay with economic fluctuations. This study introduces a novel, deep learning-based multivariate framework for precision in demanding landscapes. The framework consistently outperforms eight established benchmark models by employing vital economic indicators like GDP and port tonnage, identified through rigorous predictor importance analysis of an initial set of four variables, including imports and exports. Statistical significance is demonstrably achieved through the Diebold–Mariano and Wilcoxon rank-sum tests. Utilizing the Port of Singapore as a case study, the framework offers agile adaptability for the ever-evolving global supply chain. Comprehensive analyses ensure robustness, decoding intricate throughput dynamics. Incorporating noise-assisted multivariate empirical mode decomposition (NA-MEMD) for nonlinear decomposition and bidirectional long short-term memory (BiLSTM) for time series dependencies, this innovative approach holds promise for revolutionizing container throughput forecasting and enhancing competitiveness in the global market through optimized resource allocation and streamlined operations.

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基于深度学习的集装箱吞吐量预测多变量分解和集合框架
传统的线性模型难以捕捉集装箱动态吞吐量之间错综复杂的关系及其与经济波动之间复杂的相互作用。本研究介绍了一种新颖的、基于深度学习的多变量框架,可在要求苛刻的环境中实现精确性。该框架采用了 GDP 和港口吨位等重要经济指标,通过对包括进出口在内的初始四组变量进行严格的预测重要性分析,确定了这些指标,其性能始终优于八个既定的基准模型。通过 Diebold-Mariano 和 Wilcoxon 秩和检验,统计意义明显。以新加坡港为案例,该框架为不断变化的全球供应链提供了灵活的适应性。综合分析确保了稳健性,解码了错综复杂的吞吐量动态。这种创新方法结合了用于非线性分解的噪声辅助多变量经验模式分解(NA-MEMD)和用于时间序列依赖性的双向长短期记忆(BiLSTM),有望彻底改变集装箱吞吐量预测,并通过优化资源配置和简化操作提高在全球市场的竞争力。
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来源期刊
CiteScore
5.40
自引率
5.90%
发文量
91
期刊介绍: The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
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