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Does Interval Knowledge Sharpen Forecasting Models? Evidence from China's Typical Ports
Huang, Anqiang1; Lai, Kin Keung2; Qiao, Han3; Wang, Shouyang3,4; Zhang, Zhenji1
2018-03-01
Source PublicationINTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING
ISSN0219-6220
Volume17Issue:2Pages:467-483
AbstractSubstantial 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.
KeywordContainer throughput forecasting interval knowledge SARIMA SVR
DOI10.1142/S0219622017500456
Language英语
Funding ProjectNational Natural Science Foundation of China[71373262] ; National Natural Science Foundation of China[71390330] ; National Natural Science Foundation of China[71390331] ; National Natural Science Foundation of China[71132008] ; National Natural Science Foundation of China[71390334] ; "EC-China Research Network on Integrated Container Supply Chains" Project[612546] ; Fundamental Research Funds for the Central Universities[B15JB00040]
WOS Research AreaComputer Science ; Operations Research & Management Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications ; Operations Research & Management Science
WOS IDWOS:000428527400002
PublisherWORLD SCIENTIFIC PUBL CO PTE LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/30152
Collection系统科学研究所
Affiliation1.Beijing Jiaotong Univ, Sch Econ & Management, Beijing 100044, Peoples R China
2.City Univ Hong Kong, Dept Management Sci, Kowloon Tong, Hong Kong, Peoples R China
3.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Huang, Anqiang,Lai, Kin Keung,Qiao, Han,et al. Does Interval Knowledge Sharpen Forecasting Models? Evidence from China's Typical Ports[J]. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING,2018,17(2):467-483.
APA Huang, Anqiang,Lai, Kin Keung,Qiao, Han,Wang, Shouyang,&Zhang, Zhenji.(2018).Does Interval Knowledge Sharpen Forecasting Models? Evidence from China's Typical Ports.INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING,17(2),467-483.
MLA Huang, Anqiang,et al."Does Interval Knowledge Sharpen Forecasting Models? Evidence from China's Typical Ports".INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 17.2(2018):467-483.
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