Coupling Firefly Algorithm and Least Squares Support Vector Regression for Crude Oil Price Forecasting

2013 ◽  
Author(s):  
Xinxie Li ◽  
Lean Yu ◽  
Ling Tang ◽  
Wei Dai
Keyword(s):  
Crude Oil ◽  
Least Squares ◽  
Support Vector Regression ◽  
Firefly Algorithm ◽  
Oil Price ◽  
Support Vector ◽  
Price Forecasting ◽  
Crude Oil Price

scholarly journals Crude Oil Price Forecasting Using Hybrid Support Vector Machine

2020 ◽  
Vol 917 ◽  
pp. 012045
Author(s):  
Lee Jo Xian ◽  
Shuhaida Ismail ◽  
Aida Mustapha ◽  
Mohd Helmy Abd Wahab ◽  
Syed Zulkarnain Syed Idrus
Keyword(s):  
Support Vector Machine ◽  
Crude Oil ◽  
Oil Price ◽  
Support Vector ◽  
Price Forecasting ◽  
Crude Oil Price

AN INTEGRATED MODEL USING WAVELET DECOMPOSITION AND LEAST SQUARES SUPPORT VECTOR MACHINES FOR MONTHLY CRUDE OIL PRICES FORECASTING

2011 ◽  
Vol 07 (02) ◽  
pp. 299-311 ◽  
Author(s):  
YEJING BAO ◽  
XUN ZHANG ◽  
LEAN YU ◽  
KIN KEUNG LAI ◽  
SHOUYANG WANG
Keyword(s):  
Crude Oil ◽  
Least Squares ◽  
Hybrid Model ◽  
Wavelet Decomposition ◽  
Integrated Model ◽  
Oil Price ◽  
Forecasting Model ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Combined Forecasting

In this paper, a hybrid model integrating wavelet decomposition and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, the Haar à trous wavelet transform is first selected to decompose an original time series into several sub-series with different scales. Then the LSSVM is used to predict each sub-series. Subsequently, the final oil price forecast is obtained by reconstructing the results of the sub-series forecasts. The experimental results show that the integrated model, based on multi-scale wavelet decomposition, outperforms the traditional single-scale models. Furthermore, the proposed hybrid model is the best among all the models compared in this study. To fully integrate the advantages of several models, a combined forecasting model is presented. The study shows that the combined forecasting model is clearly better than any individual model for crude oil price forecasting.

scholarly journals Forecasting Crude Oil Price with Multiscale Denoising Ensemble Model

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
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.

scholarly journals Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Li Shu-rong ◽  
Ge Yu-lei
Keyword(s):  
Genetic Algorithm ◽  
Crude Oil ◽  
Support Vector Regression ◽  
Accurate Method ◽  
Oil Price ◽  
Support Vector ◽  
Crude Oil Price ◽  
Dynamic Correction ◽  
Comparison Of The Results ◽  
Forecasting Errors

A new accurate method on predicting crude oil price is presented, which is based onε-support vector regression (ε-SVR) machine with dynamic correction factor correcting forecasting errors. We also propose the hybrid RNA genetic algorithm (HRGA) with the position displacement idea of bare bones particle swarm optimization (PSO) changing the mutation operator. The validity of the algorithm is tested by using three benchmark functions. From the comparison of the results obtained by using HRGA and standard RNA genetic algorithm (RGA), respectively, the accuracy of HRGA is much better than that of RGA. In the end, to make the forecasting result more accurate, the HRGA is applied to the optimize parameters ofε-SVR. The predicting result is very good. The method proposed in this paper can be easily used to predict crude oil price in our life.

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