An Adaptive Hybrid Learning Paradigm Integrating CEEMD, ARIMA and SBL for Crude Oil Price Forecasting

Jiang Wu · Yu Chen · Tengfei Zhou · Taiyong Li

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Stock Market Forecasting Methods Forecasting Techniques and Applications Energy Load and Power Forecasting

Abstract

Crude oil is one of the main energy sources and its prices have gained increasing attention due to its important role in the world economy. Accurate prediction of crude oil prices is an important issue not only for ordinary investors, but also for the whole society. To achieve the accurate prediction of nonstationary and nonlinear crude oil price time series, an adaptive hybrid ensemble learning paradigm integrating complementary ensemble empirical mode decomposition (CEEMD), autoregressive integrated moving average (ARIMA) and sparse Bayesian learning (SBL), namely CEEMD-ARIMA&SBL-SBL (CEEMD-A&S-SBL), is developed in this study. Firstly, the decomposition method CEEMD, which can reduce the end effects and mode mixing, was employed to decompose the original crude oil price time series into intrinsic mode functions (IMFs) and one residue. Then, ARIMA and SBL with combined kernels were applied to predict target values for the residue and each single IMF independently. Finally, the predicted values of the above two models for each component were adaptively selected based on the training precision, and then aggregated as the final forecasting results using SBL without kernel-tricks. Experiments were conducted on the crude oil spot prices of the West Texas Intermediate (WTI) and Brent crude oil to evaluate the performance of the proposed CEEMD-A&S-SBL. The experimental results demonstrated that, compared with some state-of-the-art prediction models, CEEMD-A&S-SBL can significantly improve the prediction accuracy of crude oil prices in terms of the root mean squared error (RMSE), the mean absolute percent error (MAPE), and the directional statistic (Dstat).

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References

1994Comparing Predictive Accuracy 1995Comparing Predictive Accuracy 2008Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm 2010Forecasting crude oil market volatility: Further evidence using GARCH-class models 2012Real-Time Forecasts of the Real Price of Oil 2010International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models 2011Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling 2015A novel hybrid method for crude oil price forecasting

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