Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Filters applied. Clear all
. 2014;2014:341734.
doi: 10.1155/2014/341734. Epub 2014 Jun 25.

Day-ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

Free PMC article

Day-ahead Crude Oil Price Forecasting Using a Novel Morphological Component Analysis Based Model

Qing Zhu et al. ScientificWorldJournal. .
Free PMC article


As a typical nonlinear and dynamic system, the crude oil price movement is difficult to predict and its accurate forecasting remains the subject of intense research activity. Recent empirical evidence suggests that the multiscale data characteristics in the price movement are another important stylized fact. The incorporation of mixture of data characteristics in the time scale domain during the modelling process can lead to significant performance improvement. This paper proposes a novel morphological component analysis based hybrid methodology for modeling the multiscale heterogeneous characteristics of the price movement in the crude oil markets. Empirical studies in two representative benchmark crude oil markets reveal the existence of multiscale heterogeneous microdata structure. The significant performance improvement of the proposed algorithm incorporating the heterogeneous data characteristics, against benchmark random walk, ARMA, and SVR models, is also attributed to the innovative methodology proposed to incorporate this important stylized fact during the modelling process. Meanwhile, work in this paper offers additional insights into the heterogeneous market microstructure with economic viable interpretations.


Algorithm 1
Algorithm 1
A MCA based hybrid methodology.
Algorithm 2
Algorithm 2
Iterative thresholding algorithm for MCA.

Similar articles

See all similar articles


    1. Yang CW, Hwang MJ, Huang BN. An analysis of factors affecting price volatility of the US oil market. Energy Economics. 2002;24(2):107–119.
    1. Plourde A, Watkins GC. Crude oil prices between 1985 and 1994: how volatile in relation to other commodities? Resource and Energy Economics. 1998;20(3):245–262.
    1. Yu L, Wang S, Lai KK. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics. 2008;30(5):2623–2635.
    1. Zhang X, Lai KK, Wang S-Y. A new approach for crude oil price analysis based on empirical mode decomposition. Energy Economics. 2008;30(3):905–918.
    1. Clements MP, Franses PH, Swanson NR. Forecasting economic and financial time-series with non-linear models. International Journal of Forecasting. 2004;20(2):169–183.

Publication types

LinkOut - more resources