Crude Oil Price Forecasting Using Ensemble Empirical Mode Decomposition and Generalized Regression Neural Networks

Herawati, Sri; Latif, Muhammad

doi:10.1166/asl.2017.10652

Cited by 3 publications

(5 citation statements)

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“…The comparison between both can help to determine which method has better accuracy in predicting world oil prices. This research is a continuation of the research conducted by Sharma (1998) and Herawati and Djunaidy (2014) in terms of predicting world oil prices.…”

Section: Methodsmentioning

confidence: 66%

“…The results further show that the MAPE of the Empirical Decomposition method is better than the ARIMA. While world oil price forecasting methods are intended to accommodate the nature of crude oil prices as it tends to be nonlinear and nonstationary, and is influenced by many factors, the method that integrates the Empirical Decomposition and the Neural Network are helpful and better in predicting oil price movements (Herawati and Djunaidy, 2014). This research has illustrated that in essence, between the models of the ARIMA and the Empirical Decomposition, that both can perform the oil price forecasting against current world oil price.…”

Section: Discussionmentioning

confidence: 95%

“…There are some studies that have attempted to look at oil price forecasting. Herawati and Djunaidy (2014) had conducted one under the title of "The crude oil price forecasting using a combination of Empirical Mode Decomposition method of Ensemble (EEMD) and Neural Networks". Their results indicate that the volatility of world oil prices would occur more accurately by using the EEMD-JTS instead of the EMD-JTS.…”

Section: Previous Researchmentioning

confidence: 99%

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Black Gold Has Fallen - No More Gambling on the Prices

Andrianto¹,

Oebit²

2017

J. Fin. Bank. Review

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Objective - The crude oil, also known as black gold, is an essential commodity for the sustainability of various industries in the world. Oil prices play an important role in world economy because it causes repercussions. For example, world oil prices plummeted at the end of 2013 and its impact created fluctuations in prices which had affected world economy badly. The aim of this research is to locate a good model that can help to predict oil price fluctuations so that industries can avoid potential negative impacts. Methodology/Technique - Data of world oil prices from 1987 to 2016 were extracted from West Texas Intermediate (WTI) and Brent Oil sources. A comparative analysis using Empirical Decomposition and Autoregressive Integrated Moving Average (ARIMA) was applied toidentify differences and data were then analysed through SPSS 23. For this research, a set of models based on the smallest MAPE (Mean Absolute Percentage Error) was proposed. Findings - Results indicate that the Empirical Decomposition was a more appropriate method for predicting oil prices due to the non-linearity of oil price data. In addition, the MAPE also produced a lower error rate than the ARIMA. Novelty - In this research, world oil price volatility fromWest Texas Intermediate (WTI) and Brent Oil Price data were examined to predict oil price movement for future anticipations. Type of Paper: Empirical Keywords: Forecasting, Oil Prices, Autoregressive Integrated Moving Average, ARIMA, Empirical Decomposition, West Texas Intermediate, Brent Oil Price.

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Section: Methodsmentioning

confidence: 66%

Section: Discussionmentioning

confidence: 95%

Section: Previous Researchmentioning

confidence: 99%

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Black Gold Has Fallen - No More Gambling on the Prices

Andrianto¹,

Oebit²

2017

J. Fin. Bank. Review

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“…Multiscale models have demonstrated their superiority in forecasting nonlinear and nonstationary crude oil prices. 17,18 Such models decompose a complex times series into a few simple components, predict each component individually and finally ensembles all predicted values as the final result. 10 Al-Alimi et al provided a suitable model that can address sudden fluctuations in the energy market and different kinds of energy data sets.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, mixed single models still cannot overcome the shortcomings of the assumption that data series are nonlinear and nonstationary. Multiscale models have demonstrated their superiority in forecasting nonlinear and nonstationary crude oil prices 17,18 . Such models decompose a complex times series into a few simple components, predict each component individually and finally ensembles all predicted values as the final result 10 .…”

Section: Literature Reviewmentioning

confidence: 99%

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 tourist visits using data decomposition technique and learning optimization of artificial neural network

Herawati

Latif

2021

J. Phys.: Conf. Ser.

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Estimates of tourist visits is very important to determining policy and decision making. This study proposed a new method for forecasting tourist visits. A case study was conducted at a tourist spot in Sumenep, Indonesia. The model proposed is data decomposition and optimization of learning against tourist visits data. Data decomposed use the Ensemble Empirical Mode Decomposition (EEMD) method, then data learning use the Feedforward Neural Network (FNN) which was optimized using the Polak-Ribiere Conjugate Gradient (PCG). The two methods are integrated to produce accurate forecasts. Several patterns of learning data were carried out in this experiment. The results of this method show good permformance results as measured used RMSE and MSE.

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Crude Oil Price Forecasting Using Ensemble Empirical Mode Decomposition and Generalized Regression Neural Networks

Cited by 3 publications

References 0 publications

Black Gold Has Fallen - No More Gambling on the Prices

Black Gold Has Fallen - No More Gambling on the Prices

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

Forecasting tourist visits using data decomposition technique and learning optimization of artificial neural network

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