Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.procs.2014.05.312 | Procedia Computer Science |
Keywords | Field | DocType |
Container throughput forecast,Judgemental adjustment,Domain knowledge,MCMC algorithm | Data mining,Port (computer networking),Domain knowledge,Computer science,Artificial intelligence,Statistical model,Throughput,Data model,Machine learning | Conference |
Volume | ISSN | Citations |
31 | 1877-0509 | 1 |
PageRank | References | Authors |
0.36 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anqiang Huang | 1 | 5 | 1.88 |
| Han Qiao | 2 | 10 | 6.30 |
| Shouyang Wang | 3 | 2396 | 219.80 |