by Huang et al. (2003b). The second is by Cummings et al. (2004), to useEMD to prove the existence
of a spatial-temporal travelingwave in the incidence of dengue hemorrhagic fever in Thailand.Wewill
show that EMD can be more widely used in social sciences, such as for crude oil price analysis, later.
In this paper, we apply Ensemble EMD (EEMD,Wu and Huang, 2004), an improved EMD, to
crude oil price data and find that it can help interpret the formation of crude oil price from a novel
perspective. First, three crude oil price series with different time ranges and frequencies are
decomposed into several independent intrinsic modes, from high to low frequency. Second, the
intrinsic modes are composed into a fluctuating process, a slowly varying part and a trend based
on fine-to-coarse reconstruction. The economic meanings of the three components are identified
as short term fluctuations caused by normal supply–demand disequilibrium or some other market
activities, the effect or shock of a significant event, and a long term trend, according to their
respective scales and characteristics. Finally, we define the features of the three components and
the evolution of crude oil price. Some forecasting strategies for crude oil price are also discussed
in the end of the paper, based on our conclusions.
The rest of the paper is organized as follows: Section 2 gives a brief introduction to the basic
theory and algorithm of EMD and EEMD. Section 3 introduces the data materials and decomposes
them by EEMD. The derived intrinsic modes are also shown in this section. Detailed analyses based
on a composition of intrinsic modes are presented in Section 4. Section 5 concludes the paper.