A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting

Yu, Lean; Dai, Wei; Tang, Ling

doi:10.1016/j.engappai.2015.04.016

Cited by 174 publications

(76 citation statements)

References 53 publications

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“…Then, a fine-to-coarse reconstruction algorithm is applied to compose the obtained IMFs and residue into the high-frequency fluctuation, the low-frequency fluctuation, and trend terms (Yu et al 2015;Yu et al 2016).…”

Section: Fine-to-coarse Reconstruction Algorithmmentioning

confidence: 99%

An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China

Xiong¹,

Li²,

Bao³

2017

Agric. Econ. - Czech

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Short-term forecasting of hog price, which forms the basis for the decision making, is challenging and of great interest for hog producers and market participants. This study develops improved ensemble empirical mode decomposition (EEMD)-based hybrid approach for the short-term hog price forecasting. Specifically, the EEMD is first used to decompose the original hog price series into several intrinsic-mode functions (IMF) and one residue. The fine-to-coarse reconstruction algorithm is then applied to compose the obtained IMFs and residue into the high-frequency fluctuation, the low-frequency fluctuation, and the trend terms which can highlight new features of the hog price fluctuations. Afterwards, the extreme learning machine (ELM) is employed to model the low-frequency fluctuation, while the autoregressive integrated moving average (ARIMA) and the polynomial function are used to fit the high-frequency fluctuation and trend term, respectively, in a multistep-ahead fashion. The commonly used iterated prediction strategy is adopted for the implementation of the multistep-ahead forecasting. The monthly hog price series from January 2000 to May 2015 in China is employed to evaluate the forecasting performance of the proposed approach with the selected counterparts. The numerical results indicate that the improved EEMD-based hybrid approach is a promising alternative for the short-term hog price forecasting.

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Section: Fine-to-coarse Reconstruction Algorithmmentioning

confidence: 99%

An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China

Xiong¹,

Li²,

Bao³

2017

Agric. Econ. - Czech

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“…The standard PSO uses the same parameters as APSO. Note that to guarantee ∑ [4,27], we apply RBF kernel in LSSVR and use grid search to find the optimal γ and σ 2 in the range of {2 k , k = −4, −3, . .…”

Section: Experimental Settingsmentioning

confidence: 99%

“…For example, Yu et al proposed a model based on empirical mode decomposition (EMD) and ANN to predict WTI and Brent crude oil price, and the results demonstrated the attractiveness of the proposed model [9]. Yu et al also proposed a novel model based on ensemble EMD (EEMD) and extended extreme learning machine (EELM) to predict the crude oil price of WTI [4,30]. Zhang et al put forward a novel hybrid model with EEMD, LSSVM, particle swarm optimization (PSO), and GARCH to predict crude oil price, where LSSVM with parameters optimized by PSO and GARCH were used to forecast nonlinear and time-varying components by EEMD, respectively [26].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, a novel "decomposition and ensemble" framework has demonstrated its superiority in forecasting time series, which decomposes a complex times series into a few simple components, predicts each component individually, and finally ensembles all predicted values as final result [4,9,[26][27][28][29]. The simple components can effectively preserve some features of complex raw data from different perspectives, and each of them can be independently handled with relatively simple methods.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The application of this methodology in the crude oil price forecasting showed improved performance [16]. Yu et al [17] proposed a decomposition and ensemble learning paradigm for crude oil price forecasting [17]. The key to all of these approaches is to separate data components with unique data characteristics ex ante and to select models with the data-matching assumption.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

Zha

Chen

et al. 2016

Entropy

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Abstract:In this paper, we propose a multiscale dependence-based methodology to analyze the dependence structure and to estimate the downside portfolio risk measures in the energy markets. More specifically, under this methodology, we formulate a new bivariate Empirical Mode Decomposition (EMD) copula based approach to analyze and model the multiscale dependence structure in the energy markets. The proposed model constructs the Copula-based dependence structure formulation in the Bivariate Empirical Mode Decomposition (BEMD)-based multiscale domain. Results from the empirical studies using the typical Australian electricity daily prices show that there exists a multiscale dependence structure between different regional markets across different scales. The proposed model taking into account the multiscale dependence structure demonstrates statistically significantly-improved performance in terms of accuracy and reliability measures.

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A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting

Cited by 174 publications

References 53 publications

An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China

An improved EEMD-based hybrid approach for the short-term forecasting of hog price in China

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

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

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