CRUDE OIL PRICE FORECASTING WITH TEI@I METHODOLOGY

被引：65

作者：

WANG Shouyang Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China ^{[100080
]}

School of Management Graduate School of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China YU Lean Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China ^{[100049
,100080
]}

School of Management Graduate School of Chinese Academy of Sciences Chinese Academy of Sciences Beijing China K K LAI Department of Management Sciences City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong ^{[100049
]}

College of Business Administration Hunan University Changsha China ^{[410082
]}

机构：

来源：

JournalofSystemsScienceandComplexity | 2005年 / 02期

关键词：

TEI@I methodology; oil price forecasting; text mining; econometrics; Intelligence; integration;

D O I：

暂无

中图分类号：

F224 [经济数学方法];

学科分类号：

0701 ; 070104 ;

摘要：

<正>The 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 nonlinea

引用

页码：145 / 166

页数：22