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

Yingrui Zhou,Zijie Qian,Taiyong Li,Jiayi Shi

doi:10.1155/2019/4392785

Abstract

Crude oil is one of the most important types of energy for the global economy, and hence it is very attractive to understand the movement of crude oil prices. However, the sequences of crude oil prices usually show some characteristics of nonstationarity and nonlinearity, making it very challenging for accurate forecasting crude oil prices. To cope with this issue, in this paper, we propose a novel approach that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and extreme gradient boosting (XGBOOST), so-called CEEMDAN-XGBOOST, for forecasting crude oil prices. Firstly, we use CEEMDAN to decompose the nonstationary and nonlinear sequences of crude oil prices into several intrinsic mode functions (IMFs) and one residue. Secondly, XGBOOST is used to predict each IMF and the residue individually. Finally, the corresponding prediction results of each IMF and the residue are aggregated as the final forecasting results. To demonstrate the performance of the proposed approach, we conduct extensive experiments on the West Texas Intermediate (WTI) crude oil prices. The experimental results show that the proposed CEEMDAN-XGBOOST outperforms some state-of-the-art models in terms of several evaluation metrics.

Highlights

As one of the most important types of energy that power the global economy, crude oil has great impacts on every country, every enterprise, and even every person
The main contributions of this paper are threefold: (1) We propose a novel approach, so-called CEEMDAN-XGBOOST, for forecasting crude oil prices, following the “decomposition and ensemble” framework; (2) extensive experiments are conducted on the publicly-accessed West Texas Intermediate (WTI) to demonstrate the effectiveness of the proposed approach in terms of several evaluation metrics; (3) we further study the impacts of several parameter settings with the proposed approach
We propose a novel model, namely, CEEMDANXGBOOST, to forecast crude oil prices

Summary

Introduction

As one of the most important types of energy that power the global economy, crude oil has great impacts on every country, every enterprise, and even every person. It is a crucial task for the governors, investors, and researchers to forecast the crude oil prices accurately. The movement of crude oil prices is irregular. The movement of the crude oil prices in the last decades has shown that the forecasting task is very challenging, due to the characteristics of high nonlinearity and nonstationarity of crude oil prices

Methods

Results

Discussion

Conclusion