The determination of oil prices 1945–1995

Stevens, Paul

doi:10.1016/0301-4215(95)00092-w

Cited by 51 publications

(23 citation statements)

References 10 publications

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“…As a special commodities, crude oil is traded internationally among many different players-oil producing nations, oil companies, individual refineries, oil importing nations, and speculators. Like most commodities, crude oil price is also basically determined by its supply and demand (Hagen, 1994;Stevens, 1995), but it is strongly influenced by many irregular past/present/future events like weather, stock levels, GDP Energy Economics 30 (2008) [2623][2624][2625][2626][2627][2628][2629][2630][2631][2632][2633][2634][2635] growth, political aspects and even people's psychological expectations. Furthermore, since it takes considerable time to ship crude oil from one country to another, oil prices vary in different parts of the world.…”

Section: Introductionmentioning

confidence: 99%

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

Yu¹,

Wang²,

Lai³

2008

Energy Economics

596

330

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Section: Introductionmentioning

confidence: 99%

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

Yu¹,

Wang²,

Lai³

2008

Energy Economics

596

330

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“…Bric) and developed countries (G8) (Asif andMuneer 2007, Nakhle 2007). Also, an estimation of the oil prices is important based on existing and/or renewable energy to help find the solution to growing energy challenges (Stevens 1995, Asif and Muneer 2007, Sadarsky 2009). Measuring globalisation issues was proposed as an index based on several component indicators such as:…”

Section: Literature Review Related To Sustainabilitymentioning

confidence: 99%

DFSME: design for sustainable manufacturing enterprises (an economic viewpoint)

Garbie¹

2013

International Journal of Production Research

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Design for sustainable manufacturing enterprise (DFSME) is considered to be a new ideologue regarding survival of manufacturing enterprise and it can also be considered as one of the most important solutions to deal with the existing global financial crisis. The DFSME is a systemic approach that simultaneously determines the aspects of sustainability and how they can be aggregated taking into consideration the globalisation issues. The problem addressed in this paper is how to present the concepts of sustainability to guide manufacturing enterprises analysts and designers with the most effective aspects for analysing sustainability. These aspects are: international issues; contemporary issues; innovative products; reconfigurable manufacturing systems; manufacturing strategies; business models; flexible organisation management; manufacturing strategies and performance measurements. Based on these aspects, the sustainable model will be analysed and presented through covering all aspects of sustainable manufacturing enterprise and the sustainability assessment will be measured. The ultimate goal of this paper is to consider the needs of manufacturing enterprise throughout most of the world to be sustainable as well as to be globalised enterprises. This analysis shows that the DFSME is a very large task and should be taken into consideration as one aspect of the next industrial revolution.

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“…유가의 등락은 모든 나라들의 경제활동에 민감한 영향을 미치 는데 유가가 올라가면 OECD와 개발 도상국 같은 석유수입국 은 경제 전체의 물가가 상승해 실질적인 경기 침체로 이어지 고, 반면에 1998년 같이 유가가 하락하게 되면 중동지역의 주 요 석유 수출국은 심각한 경제 적자를 초래하게 된다 (Birol, 2004;Abosedra and Baghestan, 2004). 따라서 유가 등락을 예측 하는 것은 과거부터 계속되어온 전세계적인 이슈 중 하나이다 (Stevens, 1995). (Lynch, 2003;Basher and Sadorsky, 2006;Amano and Norden, 1998;Svensson, 2005;He et al, 2009).…”

Section: 서 론unclassified

Oil Price Forecasting Based on Machine Learning Techniques

Park¹,

Hou

Shin

2011

Journal of Korean Institute of Industrial Engineers

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Oil price prediction is an important issue for the regulators of the government and the related industries. When employing the time series techniques for prediction, however, it becomes difficult and challenging since the behavior of the series of oil prices is dominated by quantitatively unexplained irregular external factors, e.g., supply-or demand-side shocks, political conflicts specific to events in the Middle East, and direct or indirect influences from other global economical indices, etc. Identifying and quantifying the relationship between oil price and those external factors may provide more relevant prediction than attempting to unclose the underlying structure of the series itself. Technically, this implies the prediction is to be based on the vectoral data on the degrees of the relationship rather than the series data. This paper proposes a novel method for time series prediction of using Semi-Supervised Learning that was originally designed only for the vector types of data. First, several time series of oil prices and other economical indices are transformed into the multiple dimensional vectors by the various types of technical indicators and the diverse combination of the indicator-specific hyper-parameters. Then, to avoid the curse of dimensionality and redundancy among the dimensions, the wellknown feature extraction techniques, PCA and NLPCA, are employed. With the extracted features, a timepointspecific similarity matrix of oil prices and other economical indices is built and finally, Semi-Supervised Learning generates one-timepoint-ahead prediction. The series of crude oil prices of West Texas Intermediate (WTI) was used to verify the proposed method, and the experiments showed promising results : 0.86 of the average AUC. †

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The determination of oil prices 1945–1995

Cited by 51 publications

References 10 publications

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

DFSME: design for sustainable manufacturing enterprises (an economic viewpoint)

Oil Price Forecasting Based on Machine Learning Techniques

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