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 forec...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.展开更多
An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches ...An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. In this study, two artificial intelligence approaches, has been used namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). We employed in-sample forecasting on daily free-on-board CPO prices in Malaysia and the series data stretching from a period of January first, 2004 to the end of December 2011. The predictability power of the artificial intelligence approaches was also made in regard with the statistical forecasting approach such as the autoregressive fractionally integrated moving average (ARFIMA) model. The general findings demonstrated that the ANN model is superior compared to the ANFIS and ARFIMA models in predicting the CPO prices.展开更多
With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the lit...With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.展开更多
调研分析国际浮式生产储油卸油装置(Floating Production Storage and Offloading,FPSO)的市场需求现状,提出国际FPSO市场需求的预测方法。采用熵值法、人工神经网络法、随机森林回归、ADABOOST回归、ARMA模型等多算法融合的方法,对国际...调研分析国际浮式生产储油卸油装置(Floating Production Storage and Offloading,FPSO)的市场需求现状,提出国际FPSO市场需求的预测方法。采用熵值法、人工神经网络法、随机森林回归、ADABOOST回归、ARMA模型等多算法融合的方法,对国际FPSO订单进行组合预测,结果表明国际FPSO市场需求在2025年、2030年、2035年的需求量分别为9、10与12艘,可见国际FPSO市场需求在未来十多年间稳步回升。我国船企应密切关注原油价格变化对FPSO市场需求的影响,积极采用新产品技术,同时减少碳排放,提高市场份额。展开更多
基金This research is partially supported by NSFC, CAS, RGC of Hong Kong and Ministry of Education and Technology of Japan
文摘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.
文摘An accurate prediction of crude palm oil (CPO) prices is important especially when investors deal with ever-increasing risks and uncertainties in the future. Therefore, the applicability of the forecasting approaches in predicting the CPO prices is becoming the matter into concerns. In this study, two artificial intelligence approaches, has been used namely artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS). We employed in-sample forecasting on daily free-on-board CPO prices in Malaysia and the series data stretching from a period of January first, 2004 to the end of December 2011. The predictability power of the artificial intelligence approaches was also made in regard with the statistical forecasting approach such as the autoregressive fractionally integrated moving average (ARFIMA) model. The general findings demonstrated that the ANN model is superior compared to the ANFIS and ARFIMA models in predicting the CPO prices.
文摘With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.
文摘调研分析国际浮式生产储油卸油装置(Floating Production Storage and Offloading,FPSO)的市场需求现状,提出国际FPSO市场需求的预测方法。采用熵值法、人工神经网络法、随机森林回归、ADABOOST回归、ARMA模型等多算法融合的方法,对国际FPSO订单进行组合预测,结果表明国际FPSO市场需求在2025年、2030年、2035年的需求量分别为9、10与12艘,可见国际FPSO市场需求在未来十多年间稳步回升。我国船企应密切关注原油价格变化对FPSO市场需求的影响,积极采用新产品技术,同时减少碳排放,提高市场份额。
