scholarly journals A New Approach for Forecasting Crude Oil Prices Using Median Ensemble Empirical Mode Decomposition and Group Method of Data Handling

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Waqas Ahmad ◽  
Muhammad Aamir ◽  
Umair Khalil ◽  
Muhammad Ishaq ◽  
Nadeem Iqbal ◽  
...  
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Oil Price ◽  
Group Method ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Mode Decomposition ◽  
Proposed Model

The accuracy of time series forecasting is more important and can assist organizations to take up-to-date decisions for better planning and management. Several classical econometrics and computational approaches show promising results for the ordinary time series forecasting tasks, but they are not satisfactory in crude oil price forecasting. Ensemble empirical mode decomposition (EEMD) not only resolves the problem of nonlinearity and nonstationarity of time series prediction but also creates some problems (i.e., mood mixing and splitting). In this study, we proposed a new hybrid method that combines the median ensemble empirical mode decomposition and group method of data handling (MEEMD-GMDH) to reduce mood splitting problems and forecast crude oil price. MEEMD is achieved by replacing the mean operator with the median operator during the EEMD process. For testing and validation purposes of the different models, the two-seat stamp benchmarked crude oil price data are used (i.e., Brent and West Texas Intermediate (WTI)). To check the proposed model performance, different evaluation measures are used including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Diebold-Mariano (DM) test. All the forecasting accuracy measures confirmed that our proposed model performs well in crude oil prices forecasting as compared to other hybrid models.

scholarly journals Crude Oil Prices Forecasting: An Approach of Using CEEMDAN-Based Multi-Layer Gated Recurrent Unit Networks

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1543 ◽  
Author(s):  
Hualing Lin ◽  
Qiubi Sun
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Empirical Mode Decomposition ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Mode Decomposition ◽  
Gated Recurrent Unit ◽  
Mode Mixing

Accurate prediction of crude oil prices is meaningful for reducing firm risks, stabilizing commodity prices and maintaining national financial security. Wrong crude oil price forecasts can bring huge losses to governments, enterprises, investors and even cause economic and social instability. Many classic econometrics and computational approaches show good performance for the ordinary time series prediction tasks, but not satisfactory in crude oil price predictions. They ignore the characteristics of non-linearity and non-stationarity of crude oil prices data, which hinder an accurate prediction and eventually lead to poor accuracy or the wrong result. Empirical mode decomposition (EMD) and ensemble EMD (EEMD) solve the problems of non-stationary time series forecasting, but they also generate new problems of mode mixing and reconstruction errors. We propose a hybrid method that is combination of the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer gated recurrent unit (ML-GRU) neural network to solve the abovementioned issues. This not only deals with the issue of mode mixing effectively, but also makes the reconstruction error of data close to zero. Multi-layer GRU has an excellent ability of nonlinear data-fitting. The experimental results of real WTI crude oil dataset show that the proposed approach perform better in crude oil prices forecasts than some state-of-the-art models.

scholarly journals Forecasting Crude Oil Prices Using Ensemble Empirical Mode Decomposition and Sparse Bayesian Learning

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1882 ◽  
Author(s):  
Taiyong Li ◽  
Zhenda Hu ◽  
Yanchi Jia ◽  
Jiang Wu ◽  
Yingrui Zhou
Keyword(s):  
Time Series ◽  
Crude Oil ◽  
Empirical Mode Decomposition ◽  
Bayesian Learning ◽  
Oil Prices ◽  
Ensemble Empirical Mode Decomposition ◽  
Sparse Bayesian Learning ◽  
Crude Oil Prices ◽  
Mode Decomposition ◽  
Model Confidence Set

Crude oil is one of the most important types of energy and its prices have a great impact on the global economy. Therefore, forecasting crude oil prices accurately is an essential task for investors, governments, enterprises and even researchers. However, due to the extreme nonlinearity and nonstationarity of crude oil prices, it is a challenging task for the traditional methodologies of time series forecasting to handle it. To address this issue, in this paper, we propose a novel approach that incorporates ensemble empirical mode decomposition (EEMD), sparse Bayesian learning (SBL), and addition, namely EEMD-SBL-ADD, for forecasting crude oil prices, following the “decomposition and ensemble” framework that is widely used in time series analysis. Specifically, EEMD is first used to decompose the raw crude oil price data into components, including several intrinsic mode functions (IMFs) and one residue. Then, we apply SBL to build an individual forecasting model for each component. Finally, the individual forecasting results are aggregated as the final forecasting price by simple addition. To validate the performance of the proposed EEMD-SBL-ADD, we use the publicly-available West Texas Intermediate (WTI) and Brent crude oil spot prices as experimental data. The experimental results demonstrate that the EEMD-SBL-ADD outperforms some state-of-the-art forecasting methodologies in terms of several evaluation criteria such as the mean absolute percent error (MAPE), the root mean squared error (RMSE), the directional statistic (Dstat), the Diebold–Mariano (DM) test, the model confidence set (MCS) test and running time, indicating that the proposed EEMD-SBL-ADD is promising for forecasting crude oil prices.

A New Hybrid Model for Forecasting Crude Oil Price and the Techniques in the Model

2014 ◽  
Vol 974 ◽  
pp. 310-317 ◽  
Author(s):  
Jing Wen Zheng ◽  
Shi Xiao Li ◽  
Yang Kun
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Crude Oil ◽  
Hybrid Model ◽  
Oil Prices ◽  
Oil Price ◽  
Crude Oil Price ◽  
Crude Oil Prices ◽  
Mode Decomposition

Being able to predict crude oil prices with a reputation of intransigence to analysis or the directions of changing in crude oil price is of increasing value. We seek a method to forecast oil prices with precise predictions. In this paper, a hybrid model was proposed, which firstly decomposes the crude oil prices into several time series with different frequencies,then predict these time series which are not white noises, and at last integrate the predictions as the final results. We use Ensemble Empirical Mode Decomposition (EEMD) and Empirical Mode Decomposition (EMD) separately as the technique to decompose crude oil prices. Then we use Dynamic Artificial Neural Network (DAN2) and Back Propagation (BP) Neural Network separately as the technique to predict the deposed time series, and finally integrate the predictions produced by DAN2 or BP by Adaptive Linear Neural Network (ALNN) as the final result of predictions. EEMD has been proved as a very useful method to decompose the nonlinear and non-stationary time series, and DAN2, different from traditional artificial neural networks, also has obvious advantages over traditional ones. In this paper, EEMD and DAN2 are used to predict crude oil prices at the first time。 All in all, we build four models-EEMD-DAN2-ALNN, EMD-BP-ALNN, EEMD-BP-ALNN and EMD-DAN2-ALNN to test which technique, EMD or EEMD, could do better job in decomposition of crude oil prices in this kind of hybrid model and whetherDAN2 could outshine BP when used in this hybrid model. Experimental results of four hybrid models indicate EEMD-DAN2-ALNN could gives the most precise predictions of crude oil prices, and DAN2 has a better performance than traditional neural networks-BP,when used in this hybrid model and EEMD could do a better job than EMD in decomposition of crude oil prices to yield precise predictions of crude oil prices in this model.

scholarly journals Analisis Kinerja Gabungan Metode Ensemble Empirical Mode Decomposition Dan Generalized Regression Neural Network

2016 ◽  
Vol 8 (2) ◽  
pp. 132
Author(s):  
Sri Herawati ◽  
M Latif
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Empirical Mode Decomposition ◽  
Learning Process ◽  
Ensemble Empirical Mode Decomposition ◽  
Generalized Regression Neural Network ◽  
Crude Oil Prices ◽  
Mode Decomposition ◽  
Generalized Regression

Abstract—The method of time series suitable for use when it checks each data patterns systematically and has many variables, such as in the case of crude oil prices. One study that utilizes the methods of time series is the integration between Ensemble Empirical Mode Decomposition (EEMD) and neural network algorithms based on Polak-Ribiere Conjugate Gradient (PCG). However, PCG requires setting free parameters in the learning process. Meanwhile, the appropriate parameters are needed to get accurate forecasting results. This research proposes the integration between EEMD and Generalized Regression Neural Network (GRNN). GRNN has advantages, such as: does not require any parameter settings and a quick learning process. For the evaluation, the performance of the method EEMD-GRNN compared with GRNN. The experimental results showed that the method EEMD-GRNN produce better forecasting of GRNN. Keywords-Forecasting crude oil price; EEMD;GRNN.

Forecasting Crude Oil price using Artificial Neural Network model

2021 ◽  
pp. 321-326
Author(s):  
Sivaprakash J. ◽  
Manu K. S.
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Crude Oil ◽  
Back Propagation ◽  
Oil Prices ◽  
Oil Price ◽  
Back Propagation Algorithm ◽  
Crude Oil Price ◽  
Propagation Algorithm ◽  
Crude Oil Prices

In the advanced global economy, crude oil is a commodity that plays a major role in every economy. As Crude oil is highly traded commodity it is essential for the investors, analysts, economists to forecast the future spot price of the crude oil appropriately. In the last year the crude oil faced a historic fall during the pandemic and reached all time low, but will this situation last? There was analysis such as fundamental analysis, technical analysis and time series analyses which were carried out for predicting the movement of the oil prices but the accuracy in such prediction is still a question. Thus, it is necessary to identify better methods to forecast the crude oil prices. This study is an empirical study to forecast crude oil prices using the neural networks. This study consists of 13 input variables with one target variable. The data are divided in the ratio 70:30. The 70% data is used for training the network and 30% is used for testing. The feed forward and back propagation algorithm are used to predict the crude oil price. The neural network proved to be efficient in forecasting in the modern era. A simple neural network performs better than the time series models. The study found that back propagation algorithm performs better while predicting the crude oil price. Hence, ANN can be used by the investors, forecasters and for future researchers.

scholarly journals Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3603 ◽  
Author(s):  
Taiyong Li ◽  
Yingrui Zhou ◽  
Xinsheng Li ◽  
Jiang Wu ◽  
Ting He
Keyword(s):  
Crude Oil ◽  
Ridge Regression ◽  
Global Economy ◽  
Evaluation Criteria ◽  
Oil Prices ◽  
Ensemble Empirical Mode Decomposition ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Crude Oil Prices

As one of the leading types of energy, crude oil plays a crucial role in the global economy. Understanding the movement of crude oil prices is very attractive for producers, consumers and even researchers. However, due to its complex features of nonlinearity and nonstationarity, it is a very challenging task to accurately forecasting crude oil prices. Inspired by the well-known framework “decomposition and ensemble” in signal processing and/or time series forecasting, we propose a new approach that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), differential evolution (DE) and several types of ridge regression (RR), namely, ICEEMDAN-DE-RR, for more accurate crude oil price forecasting in this paper. The proposed approach consists of three steps. First, we use the ICEEMDAN to decompose the complex daily crude oil price series into several relatively simple components. Second, ridge regression or kernel ridge regression is employed to forecast each decomposed component. To enhance the accuracy of ridge regression, DE is used to jointly optimize the regularization item, the weights and parameters of each single kernel for each component. Finally, the predicted results of all components are aggregated as the final predicted results. The publicly available West Texas Intermediate (WTI) daily crude oil spot prices are used to validate the performance of the proposed approach. The experimental results indicate that the proposed approach can achieve better performance than some state-of-the-art approaches in terms of several evaluation criteria, demonstrating that the proposed ICEEMDAN-DE-RR is very promising for daily crude oil price forecasting.

scholarly journals Crude Oil Price Forecasting Based on Hybridizing Wavelet Multiple Linear Regression Model, Particle Swarm Optimization Techniques, and Principal Component Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ani Shabri ◽  
Ruhaidah Samsudin
Keyword(s):  
Principal Component Analysis ◽  
Time Series ◽  
Crude Oil ◽  
Oil Prices ◽  
Principal Component ◽  
Oil Price ◽  
Price Forecasting ◽  
Crude Oil Price ◽  
Swarm Optimization ◽  
Crude Oil Prices

Crude oil prices do play significant role in the global economy and are a key input into option pricing formulas, portfolio allocation, and risk measurement. In this paper, a hybrid model integrating wavelet and multiple linear regressions (MLR) is proposed for crude oil price forecasting. In this model, Mallat wavelet transform is first selected to decompose an original time series into several subseries with different scale. Then, the principal component analysis (PCA) is used in processing subseries data in MLR for crude oil price forecasting. The particle swarm optimization (PSO) is used to adopt the optimal parameters of the MLR model. To assess the effectiveness of this model, daily crude oil market, West Texas Intermediate (WTI), has been used as the case study. Time series prediction capability performance of the WMLR model is compared with the MLR, ARIMA, and GARCH models using various statistics measures. The experimental results show that the proposed model outperforms the individual models in forecasting of the crude oil prices series.

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