Name
Title | ||
|---|---|---|
Does Interval Knowledge Sharpen Forecasting Models? Evidence From China'S Typical Ports |
Abstract | ||
|---|---|---|
Substantial studies integrating experts' point knowledge with statistical forecasting modes have been implemented to investigate a long-lasting and disputing issue which is whether or not expert knowledge could improve forecasting performance. However, a large body of current forecasting studies neglect the application of experts' interval knowledge where experts are expected to be more competent, considering that humans do much better in fuzzy calculation like interval estimation than in accurate computation like point estimation. To fill in this gap, this paper first proposes a novel forecasting paradigm incorporating interval knowledge generated by a Delphi-based expert system into the SARIMA and SVR models. For validation purposes, the proposed paradigm is applied to several representative seaports from the top three dynamic economic regions in China. The empirical results clearly show that interval knowledge, following the proposed paradigm, significantly improves the forecasting performance. This finding implies that the proposed forecasting paradigm has the good potential to be an effective method for sharpening the statistical models for container throughput forecasting. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S0219622017500456 | INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING |
Keywords | Field | DocType |
Container throughput forecasting, interval knowledge, SARIMA, SVR | Point estimation,Sharpening,Interval estimation,Data mining,Effective method,Expert system,Fuzzy logic,Delphi,Artificial intelligence,Statistical model,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
17 | 2 | 0219-6220 |
Citations | PageRank | References |
1 | 0.35 | 12 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anqiang Huang | 1 | 5 | 1.88 |
| Kin Keung Lai | 2 | 1766 | 203.01 |
| Han Qiao | 3 | 10 | 6.30 |
| Shouyang Wang | 4 | 2396 | 219.80 |
| Zhenji Zhang | 5 | 9 | 10.09 |