摘要
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 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.
The difficulty in crude oil price forecasting, due to inherent complexity,has attracted much attention of academic researchers and business practitioners. Various methodshave been tried to solve the problem of forecasting crude oil prices. However, all of the existingmodels of prediction can not meet practical needs. Very recently, Wang and Yu proposed a newmethodology for handling complex systems ― TEI@I methodology by means of a systematic integrationof 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) aremodelled by using artificial neural network (ANN) models. In addition, the impact of irregular andinfrequent future events on crude oil price is explored using web-based text mining (WTM) andrule-based expert systems (RES) techniques. Thus, a fully novel nonlinear integrated forecastingapproach with error correction and judgmental adjustment is formulated to improve predictionperformance within the framework of the TEI@I methodology. The proposed methodology and the novelforecasting approach are illustrated via an example.
基金
This research is partially supported by NSFC, CAS, RGC of Hong Kong and Ministry of Education and Technology of Japan
关键词
世界
原油
产品价格
预测方法
市场分析
经济计量学
TEI@I methodology
oil price forecasting
text mining
econometrics
intelligence
integration