Forecasting Crude Oil Price Using Kalman Filter Based on the Reconstruction of Modes of Decomposition Ensemble Model

IEEE Access

Adnan Aslam¹  Muhammad Aamir²  Raimi Dewan³  Ani Bin Shabri⁴  Wei Gao⁵ 

[1] Department of Natural Sciences and Humanities, University of Engineering and Technology, Lahore, Pakistan;Department of Statistics, Abdul Wali Khan University Mardan, Mardan, Pakistan;Institute of Electronics and Telecommunications of Rennes, University of Rennes 1, Rennes, France;Mathematical Sciences Department, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru, Malaysia;School of Information Science and Technology, Yunnan Normal University, Kunming, China;

关键词: Average mutual information;    crude oil price;    decomposition ensemble model;    reconstruction;    deterministic and stochastic components;   

DOI  :  10.1109/ACCESS.2019.2946992

来源: DOAJ

【 摘 要 】

The modes' reconstruction into the stochastic and deterministic components is proposed for forecasting the crude oil prices with the concept of “divide and conquer” and modes reconstruction. It is to reduce the complexity in the computation and to enhance the forecasting accuracy of the decomposition ensemble technique. Under the framework of “divide and conquer”, the decomposition and ensemble methodologies of forecasting power successfully improves with the proposed model based on the modes' reconstruction. The corresponding reconstruction is using average mutual information (AMI). The proposed procedure is based on four layers i.e., complex data decomposition, reconstruction of modes into components, the prediction of each individual component and assembling the final prediction. In the proposed procedure, the modes of the stochastic component are analyzed thoroughly as it influences the prediction results significantly. For verification and illustration purposes, the case study of Brent and West Texas Intermediate (WTI) daily crude oil prices data are used, and the empirical study confirms that the outcomes outperform all the considered benchmark models, including auto-regressive integrated moving average (ARIMA) model, generalized autoregressive conditional heteroscedasticity (GARCH) model, NAÏVE model, ARIMA Kalman Filter model. This outcome is achieved, with the reconstruction decomposition ensemble (RDE) model along stochastic and deterministic components. Hence, it is concluded that the proposed model achieved higher forecasting accuracy and takes less computational time with the modes' reconstruction as opposed to using all the decompose modes.

【 授权许可】

Unknown