Improved Support Vector Machine Oil Price Forecast Model Based on Genetic Algorithm Optimization Parameters

Guo, Xiaopeng; Li, DaCheng; Zhang, Anhui

doi:10.1016/j.aasri.2012.06.082

Cited by 50 publications

(23 citation statements)

References 4 publications

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“…Guo [82] improved traditional SVR forecast precision by using genetic algorithm optimized parameter of SVR in accordance with the training data. The model is found to be effective in mapping the complexities of oil price series.…”

Section: Support Vector and Genetic Algorithmmentioning

confidence: 99%

Artificial intelligence methods for oil price forecasting: a review and evaluation

Sehgal

Pandey

2015

Energy Syst

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Artificial intelligent methods are being extensively used for oil price forecasting as an alternate approach to conventional techniques. There has been a whole spectrum of artificial intelligent techniques to overcome the difficulties of complexity and irregularity in oil price series. The potential of AI as a design tool for oil price forecasting has been reviewed in this study. The following price forecasting techniques have been covered: (i) artificial neural network, (ii) support vector machine, (iii) wavelet, (iv) genetic algorithm, and (v) hybrid systems. In order to investigate the state of artificial intelligent models for oil price forecasting, thirty five research papers (published during 2001 to 2013) had been reviewed in form of table (for ease of comparison) based on the following parameters: (a) input variables, (b) input variables selection method, (c) data characteristics (d) forecasting accuracy and (e) model architecture. This review reveals procedure of AI methods used in complex oil price related studies. The review further extended above overview into discussions regarding specific shortcomings that are associated with feature selection for designing input vector, and then concluded with future insight on improving the current state-of-the-art technology.

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Section: Support Vector and Genetic Algorithmmentioning

confidence: 99%

Artificial intelligence methods for oil price forecasting: a review and evaluation

Sehgal

Pandey

2015

Energy Syst

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“…This model combines the dynamic properties of MBPNN and Haar A Trous wavelet (HTW) decomposition. Using the combination between Genetic Algorithm and Support Vector Machine (GA-SVM) and based on RMSE (Root Mean Squared Error) criteria, Guo et al [8] proved the superiority of the proposed one over the standard SVM to forecast daily Brent oil stock price data for the period running from 2000 to 2011.…”

Section: Related Researchmentioning

confidence: 99%

“…Crude oil prices prediction is not an easy task because there are many factors that can influence their tendency such government interventions, political events, weather conditions, financial speculations, supply, inventories, demand, exchange rates, OPEC oil policy, GDP, financial shocks, price trends and stock market, dollar index, gold, heating oil spot price, etc. [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Oil price prediction using ensemble machine learning

Gabralla

Jammazi

Abraham

2013

2013 International Conference on Computing, Electrical and Electronic Engineering (Icceee)

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Abstract-Crude oil price forecasting is a challenging task due to its complex nonlinear and chaotic behavior. During the last couple of decades, both academicians and practitioners devoted proactive knowledge to address this issue. A strand of them has focused on some key factors that may influence the crude oil price prediction accuracy. This paper extends this particular branch of recent works by considering a number of influential features as inputs to test the forecasting performance of daily WTI crude oil price covering the period 4th January 1999 through 10th October 2012. Empirical results indicate that the proposed methods are efficient and warrant further research in this field.

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“…Wen et al [14] used the singular spectrum analysis to decompose the stock price into terms of the trend, the market fluctuation, and the noise with different economic features over different time horizons, and then introduced these features into the svm to make price predictions. Guo et al [5] used ga to optimize the parameter selection methods of svm in accordance with training data, and improved svm forecasting precision. Zhang et al [17] proposed a novel hybrid method to forecast crude oil price and found that the newly proposed hybrid method had a strong forecasting capability for crude oil price, due to its excellent performance in adaptation to the random sample selection, data frequency and structural breaks in samples.…”

Section: E145mentioning

confidence: 99%

Based on SARIMA-BP hybrid model and SSVM model of international crude oil price prediction research

Luo¹,

Liu²,

Wang³

2017

ANZIAMJ

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We propose two hybrid prediction models for the international crude oil price: sarima-bp hybrid model; and ssvm model. The sarimabp hybrid model combines seasonality analysis and autoregressive integrated moving average with back propagation neural network model. Contents E144 prediction accuracy, and the single sarima model has lowest prediction accuracy. Thus, the ssvm model displays a better performance in oil price prediction. Further, the ssvm model predicts nymex crude oil's closing price will approach 50 dollars per barrel in May 2016.

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Improved Support Vector Machine Oil Price Forecast Model Based on Genetic Algorithm Optimization Parameters

Cited by 50 publications

References 4 publications

Artificial intelligence methods for oil price forecasting: a review and evaluation

Artificial intelligence methods for oil price forecasting: a review and evaluation

Oil price prediction using ensemble machine learning

Based on SARIMA-BP hybrid model and SSVM model of international crude oil price prediction research

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