Shipping index fitting and influencing factors extraction based on the Window-ARIMA-SVR hybrid model

Wenyang Wang; Fan Zhang; Nan He

doi:10.1117/12.3009142

16 October 2023 Shipping index fitting and influencing factors extraction based on the Window-ARIMA-SVR hybrid model

Wenyang Wang, Fan Zhang, Nan He

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032K (2023) https://doi.org/10.1117/12.3009142
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

This paper fits the shipping index and locates the influencing factors. On the model side, we conducted from three levels—the periodicity (rolling window), linearity (ARIMA), and nonlinearity (SVR) of the shipping index based on the rolling window autoregressive moving average-support vector regression (WINDOW-ARIMA-SVR) hybrid model. Regarding data, we took Tianjin Shipping Index (TSI) as the independent variable. The explanatory variables are 100 sets of Automatic Identification System (AIS) data and macroeconomic indicators from four provinces adjacent to Tianjin Port. First, the results show that the optimal rolling window length is four months based on the Mutual Information (MI) criterion. Second, the proposed model has the best fitting accuracy for TSI compared with several mainstream models. Finally, the significant influence variables of the nonlinear part of TSI change dynamically with the window rolling, but three types of indicators, namely trade import/export volume, GDP, and the total number of ships arrival/departure in the four provinces adjacent to Tianjin Port are significant in the whole domain.

Citation Download Citation

Wenyang Wang, Fan Zhang, and Nan He "Shipping index fitting and influencing factors extraction based on the Window-ARIMA-SVR hybrid model", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032K (16 October 2023); https://doi.org/10.1117/12.3009142

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