Name
Title | ||
|---|---|---|
Applying Interval Knowledge To Facilitate Seaport Container Throughput Volume Forecasting |
Abstract | ||
|---|---|---|
Substantial studies integrating experts' knowledge with statistical forecasting models have been implemented to investigate a long-lasting and disputing issue, the extent to which expert knowledge can improve forecasting performance. However, many current studies are not capable of applying experts' interval knowledge in forecasting. Experts are expected to be more competent and confident, given that human brains do much better on fuzzy calculation like interval estimation than accurate computation like point estimation. To fill in this gap, this paper first proposes a new methodology incorporating interval knowledge which combines the interval knowledge generated by a Delphi-based expert system with the SARIMA model. For validation purposes, the proposed methodology is applied to forecast the container throughput volume of Qingdao port lying in the Bohai Rim, one of the most dynamic economic regions in China. The empirical results clearly show that interval knowledge, following the proposed methodology, significantly improves the forecasting performance. This finding implies that the proposed methodology has a potential to sharpen statistical models for container throughput forecasting. |
Year | Venue | Keywords |
|---|---|---|
2016 | 2016 INTERNATIONAL CONFERENCE ON LOGISTICS, INFORMATICS AND SERVICE SCIENCES (LISS' 2016) | container throughput forecasting, interval knowledge, SARIMA |
Field | DocType | Citations |
Point estimation,Interval estimation,Data mining,Computer science,Fuzzy logic,Expert system,Delphi,Statistical model,Throughput,Computation | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anqiang Huang | 1 | 5 | 1.88 |
| Zhenji Zhang | 2 | 9 | 10.09 |
| Guowei Hua | 3 | 20 | 6.16 |
| Xianliang Shi | 4 | 17 | 2.72 |
| Zai-Li Yang | 5 | 112 | 13.72 |