Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

Yu, Lean; Wang, Shouyang; Lai, Kin Keung

doi:10.1016/j.eneco.2008.05.003

Cited by 603 publications

(363 citation statements)

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“…Among the few, Fan et al (2008) proposes a generalized pattern matching based on genetic algorithm for multi-step-ahead prediction of crude oil prices. Yu et al (2008); Xiong et al (2013) proposes an empirical mode based on the decomposition of neural networks to forecast crude oil prices. Jammazi and Aloui (2012) uses a hybrid model for crude oil forecasting, Panella et al (2012) use a mixture of gaussian neural network to forecast energy commodity prices, and Papadimitriou et al (2014) investigates the efficiency of a support vector machines in forecasting next day electricity prices.…”

Section: Introductionmentioning

confidence: 99%

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Baruník

Křehlík

2014

SSRN Journal

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract: Terms of use: Documents inIn the past decade, the popularity of realized measures and various linear models for volatility forecasting has attracted attention in the literature on the price variability of energy markets. However, results that would guide practitioners to a specifc estimator and model when aiming for the best forecasting accuracy are missing. This paper contributes to the ongoing debate with a comprehensive evaluation of multiple-step-ahead volatility forecasts of energy markets using several popular high-frequency measures and forecasting models. To capture the complex patterns hidden to linear models commonly used to forecast realized volatility, this paper also contributes to the literature by coupling realized measures with artificial neural networks as a forecasting tool. Forecasting performance is compared across models as well as realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods covering the precrisis period, recent global turmoil of markets in 2008, and the most recent post-crisis period. We conclude that coupling realized measures with arti_cial neural networks results in both statistical and economic gains, reducing the tendency to over-predict volatility uniformly during all tested periods. Our analysis favors the median realized volatility, as it delivers the best performance and is a computationally simple alternative for practitioners.

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Section: Introductionmentioning

confidence: 99%

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Baruník

Křehlík

2014

SSRN Journal

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“…The main reason behind is the phenomenon that the traditional statistical and econometric models are built on linear assumptions, which, as a result, fail to capture the nonlinear patterns hidden in the crude oil price series (Yu et al 2008). It is the fact that the oil prices may not always adjust instantaneously to the newly available information.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, crude oil price forecasting is a very important field of research, and modeling/forecasting oil prices is hindered by its intrinsic difficulties such as the high volatility (Wang et al 2005). As the crude oil spot price series are usually considered as a nonlinear and nonstationary time series, which is interactively affected by many factors, predicting crude oil price accurately is rather challenging (Yu et al 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Crude oil is traded internationally by many different players such as the oil producing nations, oil companies, individual refineries, oil importing nations, and speculators. Although crude oil price is basically determined by its supply and demand (Hagen 1994;Stevens 1995), it is also under the influence of many irregular events like stock levels, economic growth, political aspects, political instability, the decisions implemented by OPEC, and further psychological expectations of traders (Yu et al 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Yu et al (2007) followed neural network modeling methods to forecast crude oil prices and showed that NN models provide significant gains in terms of different error criteria. Yu et al (2007Yu et al ( , 2008 proposed an efficient EMD-based neural network ensemble learning algorithm that uses feed-forward neural networks for modeling and forecasting world crude oil spot prices. Qunli et al (2009) used radial basis functions (RBF) and showed that a RBF type neural network that benefits from wavelet transformations provided better results than the linear approaches in modeling monthly crude oil prices.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach

Bildirici

Ersin

2015

Pet. Sci.

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In this study, the forecasting capabilities of a new class of nonlinear econometric models, namely, the LSTAR-LST-GARCH-RBF and MLP models are evaluated. The models are utilized to model and to forecast the daily returns of crude oil prices. Many financial time series are subjected to leptokurtic distribution, heavy tails, and nonlinear conditional volatility. This characteristic feature leads to deterioration in the forecast capabilities of traditional models such as the ARCH and GARCH models. According to the empirical findings, the oil prices and their daily returns could be classified as possessing nonlinearity in the conditional mean and conditional variance processes. Several model groups are evaluated: (i) the models proposed in the first group are the LSTAR-LST-GARCH models that are augmented with fractional integration and asymmetric power terms (FIGARCH, APGARCH, and FIAPGARCH); (ii) the models proposed in the second group are the LSTAR-LST-GARCH models further augmented with MLP and RBF type neural networks. The models are compared in terms of MSE, RMSE, and MAE criteria for in-sample and out-of-sample forecast capabilities. The results show that the LSTAR based and neural network augmented models provide important gains over the single-regime baseline GARCH models, followed by the LSTAR-LST-GARCH type models in terms of modeling and forecasting volatility in crude oil prices.

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A hybrid crude oil price forecasting framework: Modified ensemble empirical mode decomposition and hidden Markov regression

Lin,

Xie,

Yang

2024

Energy Science & Engineering

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The considerable influence of crude oil prices on the international economy has motivated numerous scholars to develop various prediction models. Two difficulties are encountered in forecasting. One is that the time series of crude oil prices show massive jumps in high frequency. The other is that the time series of crude oil prices are characterised by nonlinearity, structural breaks and highly volatile states. Therefore, we propose a hybrid model that incorporates the principle of decomposition–integration. Its overall process can be divided into the steps of decomposition and integration. In decomposition step, modified ensemble empirical mode decomposition is used to decompose the data on crude oil prices. Next, K‐means and principal component analysis are utilised to extract the optimal intrinsic mode function (IMF) from diverse frequencies amongst decomposed IMFs. Following decomposition, different forecasting methods are used to match IMFs with various frequencies for prediction. In accordance with structural breaks and the highly volatile states of high‐frequency series, hidden Markov regression is applied to describe predicted values in a probabilistic manner, providing objective interpretation and forecasting. A neural network is taken to predict intermediate‐frequency IMFs by using a fully connected network. Owing to the low frequency and tendency of the residual sequence to be more stable than other frequency sequences, the autoregressive integrated moving average model is applied as the forecasting method for prediction. Finally, all individual predictions for the eventual results are ensembled as final predictions. Results confirm that the proposed model is better than several normal hybrid and single models in terms of prediction accuracy and Diebold–Mariano test results. All results confirm that the newly proposed hybrid model is a promising tool for the analysis and forecasting of crude oil prices.

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Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

Cited by 603 publications

References 27 publications

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Coupling High-Frequency Data with Nonlinear Models in Multiple-Step-Ahead Forecasting of Energy Markets' Volatility

Forecasting volatility in oil prices with a class of nonlinear volatility models: smooth transition RBF and MLP neural networks augmented GARCH approach

A hybrid crude oil price forecasting framework: Modified ensemble empirical mode decomposition and hidden Markov regression

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