Forecasting oil prices: New approaches

Medeiros, Rennan Kertlly de; Besarria, Cássio da Nóbrega; Jesús, Diego; Albuquerquemello, Vinícius Phillipe de

doi:10.1016/j.energy.2021.121968

Cited by 17 publications

(3 citation statements)

References 47 publications

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“…The impact of low oil prices on banks in Kuwait found that oil price movements have only a small impact on deposits and have no impact on credit growth. In contrast to the study, (Kertlly de Medeiros et al, 2022;Xiao et al, 2022) found that the decline in oil prices had an impact on slowing down banking loans and deposits. The decline in oil prices also had an impact on increasing the bank's bad-cost ratio.…”

Section: Literature Reviewcontrasting

confidence: 60%

World Oil Prices and Exchange Rates on Islamic Banking Risks

Ryandono

Imron

Wildan

2022

IJEEP

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Islamic banking risks are influenced by many factors, facing internal and external banking risks. In this study, the internal banking risks are represented by Credit risk as measured by Non-Performing Financing (NPF) and liquidity risks as measured by Financing to Deposit Ratio (FDR). At the same time, world oil prices and exchange rates represent the external banking risks. World oil price data is obtained from OPEC Price, and exchange rate data is obtained from the Pacific Exchange Rate Service. While the data from NPF and FDR is obtained from BI Statistics of Sharia Banking. The results of this study found that world oil prices have a significant effect on credit risk and liquidity risk in Islamic banking. In contrast, the exchange rate only has a significant effect on credit risk in Islamic banking.

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Section: Literature Reviewcontrasting

confidence: 60%

World Oil Prices and Exchange Rates on Islamic Banking Risks

Ryandono

Imron

Wildan

2022

IJEEP

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“…The general concept is the standard deviation of the difference between the forecast and actual data. The RMSE equation is given below as Equation ( 28) [279]. As the name implies, this criterion is the same as the previous but in the normalized state [6].…”

Section: Root Mean Square Error (Rmse)mentioning

confidence: 99%

Machine Learning and Deep Learning in Energy Systems: A Review

et al. 2022

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With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms, which are usually more accurate and less error, the use of these algorithms increases the ability of the model to solve complex problems in this field. In this article, we have tried to examine DL algorithms that are very powerful in problem solving but have received less attention in other studies, such as RNN, ANFIS, RBN, DBN, WNN, and so on. This research uses knowledge discovery in research databases to understand ML and DL applications in energy systems’ current status and future. Subsequently, the critical areas and research gaps are identified. In addition, this study covers the most common and efficient applications used in this field; optimization, forecasting, fault detection, and other applications of energy systems are investigated. Attempts have also been made to cover most of the algorithms and their evaluation metrics, including not only algorithms that are more important, but also newer ones that have received less attention.

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“…Many other models also have been studied for crude oil price forecasting, which can be found in Ref. [ 24 ]. Although the above model can realize the prediction of energy futures prices to some extent, the prediction accuracy is not satisfactory due to the solidification of the model and the defects of the traditional algorithm.…”

Section: Introductionmentioning

confidence: 99%