Abstract
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.
Highlights
Academic Editor: Bin Liu e accuracy of time series forecasting is more important and can assist organizations to take up-to-date decisions for better planning and management
Motivated by the potential of median ensemble empirical mode decomposition (MEEMD) in signal decomposition, we proposed a new method for the prediction of crude oil prices combining MEEMD, namely, the median ensemble empirical mode decomposition and group method of data handling (MEEMD-Group Method of Data Handling (GMDH)), stimulated by the capability of MEEMD in the signal breakdown in order to minimize mood splitting and mixing problems
Daily crude oil prices of time series data are utilized, that is, West Texas Intermediate (WTI) and Brent. e WTI series consists of 8000 observations from Feb 10, 1989, to Oct 10, 2019; 80 percent (6400 observations) are used as a training set while 20 percent (1600 observations) are used as a testing set. e Brent dataset consists of 12000 observations from Dec 10, 1973, to Oct 10, 2019; 80 percent (9600 observations) are used as a training set whereas 20 percent (2400 observations) are used as an assessment set to check the model performances. e distribution of training and
Summary
Academic Editor: Bin Liu e accuracy of time series forecasting is more important and can assist organizations to take up-to-date decisions for better planning and management. 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. Goods are vital for global growth from the perspective of nations and governments and have a significant strategic effect on national economic stability. E effective fluctuation of the value of goods helps farmers to accurately schedule production, minimize the cost, and achieve greater profitability. By creating a model of projection for consumer goods and a method of mitigation fluctuations in prices, trading firms are able to avoid risks and lower trade losses. Sharp oil price value improvements are most likely going to shake aggregate economic activity, especially since Jan 2004, the world’s oil cost has been rising rapidly and is creating striking fluctuations for the world economy. Unss oil prices are a source of major zeal for many analysts, research experts, and organizations. e price of crude oil is essentially dictated by its demand and supply but is more clearly affected by numerous unpredictable past/present/ future occurrences, such as climate change, stock levels, GDP development, and political perspectives. ese realities lead to a distinctly varying and nonlinear market and the basic component of maintaining the intricate dynamic is not understood
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