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crudeoilpriceforecastingwithteiimethodology
K K Lai1; Wang Shouyang2; Yu Lean2
2005
Source Publicationjournalofsystemsscienceandcomplexity
ISSN1009-6124
Volume018Issue:002Pages:145
AbstractThe difficulty in crude oil price forecasting, due to inherent complexity, has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting crude oil prices. However, all of the existing models of prediction can not meet practical needs. Very recently, Wang and Yu proposed a new methodology for handling complex systems-TEI@I methodology by means of a systematic integration of text mining, econometrics and intelligent techniques.Within the framework of TEI@I methodology, econometrical models are used to model the linear components of crude oil price time series (i.e., main trends) while nonlinear components of crude oil price time series (i.e., error terms) are modelled by using artificial neural network (ANN) models. In addition, the impact of irregular and infrequent future events on crude oil price is explored using web-based text mining (WTM) and rule-based expert systems (RES) techniques. Thus, a fully novel nonlinear integrated forecasting approach with error correction and judgmental adjustment is formulated to improve prediction performance within the framework of the TEI@I methodology. The proposed methodology and the novel forecasting approach are illustrated via an example.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/36739
Collection系统科学研究所
Affiliation1.香港城市大学
2.中国科学院数学与系统科学研究院
Recommended Citation
GB/T 7714
K K Lai,Wang Shouyang,Yu Lean. crudeoilpriceforecastingwithteiimethodology[J]. journalofsystemsscienceandcomplexity,2005,018(002):145.
APA K K Lai,Wang Shouyang,&Yu Lean.(2005).crudeoilpriceforecastingwithteiimethodology.journalofsystemsscienceandcomplexity,018(002),145.
MLA K K Lai,et al."crudeoilpriceforecastingwithteiimethodology".journalofsystemsscienceandcomplexity 018.002(2005):145.
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