Abstract

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.

Highlights

  • Crude oil is one of the dominant sources of energy that powers the global economy

  • The original crude oil prices and corresponding components decomposed by ensemble empirical mode decomposition (EEMD) of West Texas Intermediate (WTI) and Brent are shown in Figures 2 and 3, respectively

  • Traditional methods including statistical methods and artificial intelligence (AI)-based models usually cannot achieve satisfactory results when the forecasting is performed on raw crude oil prices

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Summary

Introduction

Crude oil is one of the dominant sources of energy that powers the global economy. The demand for crude oil will continue to increase, its pace of growth is expected to slow gradually, according to the British Petroleum (BP) energy outlook 2017 [1]. Baumeister and Kilian demonstrated that VAR models could achieve good results when forecasting crude oil prices at short horizons [6]. Wang and Wu forecasted the volatility of crude oil prices using multivariate and univariate GARCH-class models, and the results indicated that the multivariate models showed better performance than univariate models [13]. Several ARIMA-GRARCHmodels for forecasting the volatility of crude oil prices were studied in 11 markets, and the forecasting results indicated that one of the models named APARCHoutperformed the others in most cases [14]. Due to the high nonlinearity and nonstationarity, it is usually hard for these models to be directly applied to crude oil price forecasting to achieve satisfactory results. The AI models have attracted increasing interest for crude oil price forecasting because they can capture the intrinsic features of nonlinearity and nonstationarity that exist widely in crude oil prices

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