A new hybrid model for forecasting Brent crude oil price

Abdollahi, Hooman; Ebrahimi, Seyed Babak

doi:10.1016/j.energy.2020.117520

Cited by 92 publications

(29 citation statements)

References 54 publications

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“…The GA-ELM also follows the GA-SSA-ANFIS in terms of its mean absolute error, while the GA-ANFIS produces the second-highest R 2 ; however, it is approximately 3% smaller compared with the GA-SSA-ANFIS one. These results prove the sizable efficiency of the approaches, which combine genetic algorithms with other recently developed techniques, such as ANFIS, in predicting crude oil price volatility (Abd Abdollahi and Ebrahimi 2020).…”

Section: Energy Commoditiesmentioning

confidence: 62%

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic algorithms (i.e., joining them with other econometric methods). Such an approach helps to reduce each of the individual method flaws and yields promising results. In this article, three groups of commodities are discussed: energy commodities, metals, and agricultural products. The advantages and disadvantages of genetic algorithms and their hybrids are presented, and further conclusions concerning their possible improvements and other future applications are discussed. This article fills a significant literature gap, focusing on particular financial and economic applications. In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.

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Section: Energy Commoditiesmentioning

confidence: 62%

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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“…Considering the effect of energy use on economic growth in the selected developing countries, paying attention to energy consumption is a fundamental factor in warranting rapid and continuous economic growth [90]. Therefore, it is not necessary to lessen the consumption of energy to achieve CO2 reduction because it results in declining GDP.…”

Section: Table 6 Results Of Spatial Autoregressive Model Estimationmentioning

confidence: 99%

Investigating Energy Use, Environment Pollution, and Economic Growth in Developing Countries

Abdollahi

2020

Environmental and Climate Technologies

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AbstractAttaining continuous economic growth entails special consideration of energy sector and the environment. Compliance with this purpose may be more intricate in the uncertain milieu of developing countries. The present paper examines the nature of causality between energy consumption, environment pollution, and economic growth in 8 contiguous developing countries, considering GDP per capita, CO2 emissions, energy use, labour force, total population, urban population, capital formation, financial development, and trade openness. The author applied spatial simultaneous equations for random effects panel data to investigate the spatial interactions of adjacent countries over the period from 1998 to 2011. The findings reveal that energy consumption, environment degradation, and economic growth of a country influence those of its neighbours. Additionally, the results document bidirectional causal relationship between economic growth and environment pollution, as well as between environment pollution and energy consumption. Thus, there is a bidirectional relationship between energy use and economic growth. Fossil fuels replacement with renewable energy and usage of tax instruments to reduce greenhouse gas are recommended.

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“…In recent years, numerous methods for time series predictions have been proposed [2][3][4][5][6][7][8][9][10][11][12][13]. ese methods can be classified into the following three categories: traditional econometric models, machine learning approaches and deep learning models.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al [3] proposed two novel forecasting models based on ARIMA, which was employed to forecast two sections of actual wind speed series. Abdollahi and Ebrahimi [4] established a new composite model to predict Brent crude oil prices by integrating the adaptive neuro fuzzy inference system (ANFIS), autoregressive fractionally integrated moving average (ARFIMA), and Markov-switching models. However, the traditional econometric models have evident shortcomings.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Forecasting Model Based on SW‐LSTM and Wavelet Packet Decomposition: A Case Study of Oil Futures Prices

Wang

2021

Computational Intelligence and Neuroscience

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The crude oil futures prices forecasting is a significant research topic for the management of the energy futures market. In order to optimize the accuracy of energy futures prices prediction, a new hybrid model is established in this paper which combines wavelet packet decomposition (WPD) based on long short-term memory network (LSTM) with stochastic time effective weight (SW) function method (WPD-SW-LSTM). In the proposed framework, WPD is a signal processing method employed to decompose the original series into subseries with different frequencies and the SW-LSTM model is constructed based on random theory and the principle of LSTM network. To investigate the prediction performance of the new forecasting approach, SVM, BPNN, LSTM, WPD-BPNN, WPD-LSTM, CEEMDAN-LSTM, VMD-LSTM, and ST-GRU are considered as comparison models. Moreover, a new error measurement method (multiorder multiscale complexity invariant distance, MMCID) is improved to evaluate the forecasting results from different models, and the numerical results demonstrate that the high-accuracy forecast of oil futures prices is realized.

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A new hybrid model for forecasting Brent crude oil price

Cited by 92 publications

References 54 publications

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Investigating Energy Use, Environment Pollution, and Economic Growth in Developing Countries

A New Hybrid Forecasting Model Based on SW‐LSTM and Wavelet Packet Decomposition: A Case Study of Oil Futures Prices

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