Work

Research on Time Series Forecasting Method Based on Autoregressive Integrated Moving Average Model with Zonotopic Kalman Filter

Year: 2025

Type: article

Abstract: Ningbo Zhoushan Port and Shanghai Port, as the top two ports in China in terms of port cargo throughput, play a crucial role in facilitating international trade and shipping. The accurate forecasting of the cargo throughput at these ports is essential for the government planning of port infrastructure and for the efficient allocation of resources by shipping enterprises. This study proposes a novel combined forecasting method for port cargo throughput, integrating the Autoregressive Integrated Moving Average (ARIMA) model with the zonotopic Kalman filter (ZKF) to address the limitations of traditional forecasting models in terms of accuracy and timeliness. First, an ARIMA model is established to perform the preliminary forecasting of the cargo throughput time series, generating a state–space representation that captures the underlying patterns in the data. Subsequently, the ZKF is applied to filter the ARIMA predictions, dynamically adjusting the forecast intervals based on the hypercube feasible set to optimize the estimation of port throughput. The results indicate that the ARIMA–ZKF combined model significantly mitigates the effects of asynchrony and lag, achieving a high prediction accuracy and robustness. This innovative approach offers an effective new method for forecasting port throughput, providing valuable practical guidance for port development and resource management.

Source: Sustainability

DOI: 10.3390/su17072993

Authors: Xiaopu Zhang, Wenbin Cao

Institution: Jiangnan University

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDGs: Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,896