Short-term forecast of port cargo throughput based on ARIMA-RBF neural network

Yongcen Shen

doi:10.1117/12.2628147

18 February 2022 Short-term forecast of port cargo throughput based on ARIMA-RBF neural network

Yongcen Shen

Proceedings Volume 12162, International Conference on High Performance Computing and Communication (HPCCE 2021); 121621I (2022) https://doi.org/10.1117/12.2628147
Event: 2021 International Conference on High Performance Computing and Communication, 2021, Guangzhou, China

Abstract

Based on the historical data of Jiaxing Port, this paper uses ARIMA-RBF to predict the monthly cargo throughput of Jiaxing Port, builds an ARIMA-RBF model, and finds the optimal network parameters through training. On this basis, the ARIMA-RBF model is used to predict the cargo throughput of Jiaxing Port, and the actual data analysis is used to compare the ARIMA-RBF neural network, grey system theory and ARIMA prediction results. Finally, the prediction results of different models are compared to prove that the ARIMA-RBF neural network prediction is the optimal prediction model.

Citation Download Citation

Yongcen Shen "Short-term forecast of port cargo throughput based on ARIMA-RBF neural network", Proc. SPIE 12162, International Conference on High Performance Computing and Communication (HPCCE 2021), 121621I (18 February 2022); https://doi.org/10.1117/12.2628147

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