Kernel Functions for the Support Vector Machine: Comparing Performances on Crude Oil Price Data

International crude oil price is the referential scale of spot crude oil price and refined oil price. This paper made an analysis and prediction of Brent crude oil price by ARIMA model based on its price data from November 2012 to April 2013. It indicated that model ARIMA (1,1,1) possessed good prediction effect and can be used as short-term prediction of International crude oil price.

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Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model

Mathematical Problems in Engineering ◽

10.1155/2014/716571 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Xia Li ◽

Kaijian He ◽

Kin Keung Lai ◽

Yingchao Zou

Keyword(s):

Crude Oil ◽

Support Vector Regression ◽

Empirical Studies ◽

Wavelet Denoising ◽

Least Square ◽

Oil Price ◽

Support Vector ◽

Bench Mark ◽

Crude Oil Price ◽

Accuracy Requirement

Crude oil price becomes more volatile and sensitive to increasingly diversified influencing factors with higher level of deregulations worldwide. Current methodologies are being challenged as they have been constrained by traditional approaches assuming homogeneous time horizons and investment strategies. Approximations they provided over the long term time horizon no longer satisfy the accuracy requirement at shorter term and more microlevels. This paper proposes a novel crude oil price forecasting model based on the wavelet denoising ARMA models ensemble by least square support vector regression with the reduced forecasting matrix dimensions by independent component analysis. The proposed methodology combines the multi resolution analysis and nonlinear ensemble framework. The wavelet denoising based algorithm is introduced to separate and extract the underlying data components with distinct features, corresponding to investors with different investment scales, which are modeled with time series models of different specifications and parameters. Then least square support vector regression is introduced to nonlinearly ensemble results based on different wavelet families to further reduce the estimation biases and improve the forecasting generalizability. Empirical studies show the significant performance improvement when the proposed model is tested against the bench-mark models.

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Predicting Crude Oil Price Using Fuzzy Rough Set and Bio-Inspired Negative Selection Algorithm

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2019100102 ◽

2019 ◽

Vol 10 (4) ◽

pp. 25-37

Author(s):

Ayodele Lasisi ◽

Nasser Tairan ◽

Rozaida Ghazali ◽

Wali Khan Mashwani ◽

Sultan Noman Qasem ◽

...

Keyword(s):

Crude Oil ◽

Rough Set ◽

Negative Selection ◽

Nearest Neighbor ◽

Oil Price ◽

Support Vector ◽

Crude Oil Price ◽

Fuzzy Rough Set ◽

Negative Selection Algorithm ◽

Nested Generalized Exemplars

The need to accurately predict and make right decisions regarding crude oil price motivates the proposition of an alternative algorithmic method based on real-valued negative selection with variable-sized detectors (V-Detectors), by incorporating with fuzzy-rough set feature selection (FRFS) for predicting the most appropriate choices. The objective of this study is enhancing the performance of V-Detectors using FRFS for prices of crude oil. Applying FRFS serves to prune the number of features by retaining the most informative and critical features. The V-Detectors then trains and tests the features. Different radius values are applied for V-Detectors. Experimental outcome in comparison with established algorithms such as support vector machine, naïve bayes, multi-layer perceptron, J48, non-nested generalized exemplars, IBk, fuzzy-roughNN, and vaguely quantified nearest neighbor demonstrates that FRFS-V-Detectors is proficient and valuable for insightful knowledge on crude oil price. Thus, it can assist in establishing oil price market policies on the international scale.

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