An ICA-based support vector regression scheme for forecasting crude oil prices

Fan, Liwei; Pan, Sijia; Li, Zimin; Li, Huiping

doi:10.1016/j.techfore.2016.04.027

Cited by 73 publications

(34 citation statements)

References 40 publications

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“…For single models, we compare XGBOOST with one statistical model, ARIMA, and two widely used AI-models, SVR and FNN. Since the existing research has shown that EEMD significantly outperforms EMD in forecasting crude oil prices [24,31], in the experiments, we only compare CEEMDAN with EEMD. Therefore, we compare the proposed CEEMDAN-XGBOOST with EEMD-SVR, EEMD-FNN, EEMD-XGBOOST, CEEMDAN-SVR, and CEEMDAN-FNN.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…In the first stage, the original time series was decomposed into several components. Typical decomposition methodologies include wavelet decomposition (WD), independent component analysis (ICA) [24], variational mode decomposition (VMD) [25], empirical mode decomposition (EMD) [2,26] and its extension (ensemble EMD (EEMD)) [27,28], and complementary EEMD (CEEMD) [29]. In the second stage, some statistical or AI-based methodologies are applied to forecast each decomposed component individually.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, various researchers have devoted efforts to forecasting crude oil prices following the framework of "decomposition and ensemble." Fan et al put forward a novel approach that integrates independent components analysis (ICA) and support vector regression (SVR) to forecast crude oil prices, and the experimental results verified the effectiveness of the proposed approach [24]. Yu et al used EMD to decompose the sequences of the crude oil prices into several intrinsic mode functions (IMFs) at first and then used a three-layer feed-forward neural network (FNN) model for predicting each IMF.…”

Section: Introductionmentioning

confidence: 99%

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A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Zhou

Shi

et al. 2019

Complexity

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Crude oil is one of the most important types of energy for the global economy, and hence it is very attractive to understand the movement of crude oil prices. However, the sequences of crude oil prices usually show some characteristics of nonstationarity and nonlinearity, making it very challenging for accurate forecasting crude oil prices. To cope with this issue, in this paper, we propose a novel approach that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme gradient boosting (XGBOOST), so-called CEEMDAN-XGBOOST, for forecasting crude oil prices. Firstly, we use CEEMDAN to decompose the nonstationary and nonlinear sequences of crude oil prices into several intrinsic mode functions (IMFs) and one residue. Secondly, XGBOOST is used to predict each IMF and the residue individually. Finally, the corresponding prediction results of each IMF and the residue are aggregated as the final forecasting results. To demonstrate the performance of the proposed approach, we conduct extensive experiments on the West Texas Intermediate (WTI) crude oil prices. The experimental results show that the proposed CEEMDAN-XGBOOST outperforms some state-of-the-art models in terms of several evaluation metrics.

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Section: Parameter Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

Zhou

Shi

et al. 2019

Complexity

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“…Liwei et al [4] employed independent component analysis to analyze crude oil price which was decomposed into several independent components corresponding to different types of influential factors affecting oil price. 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.…”

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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“…Tang et al integrated complementary EEMD (CEEMD) and EELM to forecast crude oil price [27]. In addition, Fan et al used independent component analysis (ICA) to decompose the crude oil price time series into three independent components, and then constructed three SVR models to predict the components respectively, and finally used SVR again to integrate the results by the former three SVRs as final price [31].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

Zhou

Guo

et al. 2016

Energies

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Abstract:Crude oil, as one of the most important energy sources in the world, plays a crucial role in global economic events. An accurate prediction for crude oil price is an interesting and challenging task for enterprises, governments, investors, and researchers. To cope with this issue, in this paper, we proposed a method integrating ensemble empirical mode decomposition (EEMD), adaptive particle swarm optimization (APSO), and relevance vector machine (RVM)-namely, EEMD-APSO-RVM-to predict crude oil price based on the "decomposition and ensemble" framework. Specifically, the raw time series of crude oil price were firstly decomposed into several intrinsic mode functions (IMFs) and one residue by EEMD. Then, RVM with combined kernels was applied to predict target value for the residue and each IMF individually. To improve the prediction performance of each component, an extended particle swarm optimization (PSO) was utilized to simultaneously optimize the weights and parameters of single kernels for the combined kernel of RVM. Finally, simple addition was used to aggregate all the predicted results of components into an ensemble result as the final result. Extensive experiments were conducted on the crude oil spot price of the West Texas Intermediate (WTI) to illustrate and evaluate the proposed method. The experimental results are superior to those by several state-of-the-art benchmark methods in terms of root mean squared error (RMSE), mean absolute percent error (MAPE), and directional statistic (Dstat), showing that the proposed EEMD-APSO-RVM is promising for forecasting crude oil price.

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An ICA-based support vector regression scheme for forecasting crude oil prices

Cited by 73 publications

References 40 publications

A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

A CEEMDAN and XGBOOST‐Based Approach to Forecast Crude Oil Prices

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

Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels

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