Title: Mixed-frequency data-driven forecasting port throughput: a novel attention-DeepAR-MIDAS model

Authors: Bo Shao; Xiaoli Su; Xin Li; Xingxuan Zhuo

Addresses: Department of Industrial and Systems Engineering, University of Wisconsin-Madison, Madison, USA ' School of Economics and Management, Fuzhou University, Fuzhou, China ' School of Transportation, Fuzhou University of Technology, Fuzhou, China ' School of Economics and Management, Fuzhou University, Fuzhou, China

Abstract: Forecasting cargo throughput is an essential albeit challenging task for national and port optimisation decision-making, resource allocation, and control planning. To this end, a novel forecasting model is developed for mixed-frequency data called attention-DeepAR-MIDAS (ADM) by introducing the mixed data sampling (MIDAS) technique and attention mechanism into the DeepAR forecasting algorithm in this study. The proposed ADM model is specifically designed with an attention mechanism to accurately identify and prioritise the most influential variables, both endogenous and exogenous, over time. Hence, it can effectively use the nonlinear information of mixed-frequency data, which is conducive to port throughput forecasting. Furthermore, the ADM model possesses both long-term and short-term high-precision forecasting capabilities, enabling multi-step probability forecasting and better tracking of abnormal changes in endogenous and exogenous variables of port throughput, fitting their fluctuation trends. By analysing the differences in model performance before and after improvement based on forecast accuracy metrics, probability interval testing, and DM testing methods, the ADM model achieves accurate forecasting. Finally, China's monthly port throughput forecast results also illustrate the superiority of the ADM model, which provides decision-makers with more timely, accurate, and comprehensive forecasts.

Keywords: machine learning; mixed-frequency data; nonlinearity; time-series; port throughput forecasting.

DOI: 10.1504/IJSTL.2025.147408

International Journal of Shipping and Transport Logistics, 2025 Vol.20 No.3, pp.338 - 358

Received: 25 Aug 2023
Accepted: 06 Mar 2024
Published online: 15 Jul 2025 *

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