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

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.

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

“…In recent years, more and more AI approaches were proposed for crude oil price forecasting. The main AI prediction approaches include artificial neural networks (ANN) [11,12], support vector machine (SVM) [13,14], sparse Bayesian learning (SBL) [15], extreme learning machine (ELM) [16], extreme gradient boosting (XGBoost) [17], random vector functional link neural network (RVFL) [18], recurrent neural network(RNN) [19], etc. Mostafa et al employed an ANN model to predict the crude oil price data between 2 January 1986 and 12 June 2012 [11].…”

Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm

“…Damrongkulkamjorn et al [22] introduced a new method combining ARIMA (autoregressive integrated moving average) with classical decomposition techniques. Reference [23] proposed a novel approach that incorporates ensemble empirical mode decomposition, which is widely used in time series analysis, sparse Bayesian learning for forecasting crude oil prices. Reference [24] used the system dynamics method to simulate and analyze China's energy consumption and carbon dioxide emissions under the target constraints of 2020.…”

A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices

“…If some singular points exist, the optimal model chosen by these criteria may not be the best one. Thus, we make the model confidence set (MCS) [31,45] in order to choose the optimal model convincingly.…”

“…For single models, we compare XGBOOST with one statistical model, ARIMA, and two widely used AI-models, SVR and FNN. Since the existing research has shown that EEMD significantly outperforms EMD in forecasting crude oil prices [24,31], in the experiments, we only compare CEEMDAN with EEMD. Therefore, we compare the proposed CEEMDAN-XGBOOST with EEMD-SVR, EEMD-FNN, EEMD-XGBOOST, CEEMDAN-SVR, and CEEMDAN-FNN.…”

“…Tang et al further proposed an improved approach integrating CEEMD and EELM for forecasting crude oil prices, and the experimental results demonstrated that the proposed approach outperformed all the listed state-of-the-art benchmarks [29]. Li et al used EEMD to decompose raw crude oil prices into several components and then used kernel and nonkernel sparse Bayesian learning (SBL) to forecast each component, respectively [30,31].…”