Forecasting Container Throughputs with Domain Knowledge | Zendy

Anqiang Huang | Zendy; Han Qiao | Zendy; Shouyang Wang | Zendy

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Forecasting Container Throughputs with Domain Knowledge

Author(s) -

Anqiang Huang,

Han Qiao,

Shouyang Wang

Publication year - 2014

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2014.05.312

Subject(s) - computer science , container (type theory) , throughput , domain (mathematical analysis) , domain knowledge , port (circuit theory) , order (exchange) , data mining , artificial intelligence , machine learning , telecommunications , wireless , mechanical engineering , mathematical analysis , mathematics , electrical engineering , finance , engineering , economics

In order to alleviate the limitation of traditional statistical models utilizing only structured data, this paper proposes a new fore- casting method, which is able to take full advantage of domain knowledge and avoid many kinds of biases and inconsistencies inherent in subjective judgments. The new method is applied to forecasting the container throughput of Guangzhou Port, one of the most important ports of China. In order to test the effectiveness of the new method, we compare its performance with that of the frequently-used ARIMAX model. The results show that the new method significantly outperforms the ARIMAX model

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