Comparison on Turnovers of Agricultural Products Futures Based on Hilbert-Huang Transform

Abstract:

In order to distinguish the different patterns and evolving trends on turnovers of agricultural products futures between Zhengzhou Commodity Exchange (Z-CE) and Dalian Commodity Exchange (D-CE), a novel time-frequency analysis approach, i.e. Hilbert-Huang transform (HHT), is investigated in this paper. Firstly, Hilbert-Huang transform is briefly introduced. Secondly, two different non-stationary signals of turnover of agricultural products futures from 2009 to 2013 coming from Z-CE and D-CE are described in Empirical Mode Decomposition (EMD). With these results, the two signals are distinctly different from each other. It is proved that the technique of HHT is effective for the purpose of distinction of turnover of agricultural products futures in commodity exchanges.

• Periodical: Advanced Materials Research (Volumes 989-994)
• Pages: 2713-2718
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.989-994.2713
• Online since: July 2014
• Authors: Qing Bin Han*, Hai Li Shi
• Keywords: Empirical Mode Decomposition (EMD), Hilbert-Huang Transform, Non-Stationary Signals, Turnover Amounts

References

[1] Holger, K. and Thomas, S., in: Nonlinear Time Series Analysis, edited by Cambridge University Press, 1-20(1999).

[2] He,Z.J., et al, in: Time-frequency(scale) analysis and diagnosis for nonstationary dynamic signal of machinery, edited by International Journal of Plant Engineering and Management, 1(1), 40-47(1996).

[3] Meltzer, G. S. and Ivanov ,Y. Y., in: Fault detection in gear drives with non-stationary rotational speed- Part I: the time-frequency approach, edited by Mechanical Systems and Signal Processing, 17(5), 1033-1047(2003).

DOI: 10.1006/mssp.2002.1530

[4] Wang W.Y., and Harrap,M. J., in: Condition monitoring of rolling element bearings by using cone kernel time-frequency distribution, edited by Processing of the SPIE Conference on Measurement Technology and Intelligent Instrument. Bellingham, WA, 290-298(1993).

DOI: 10.1117/12.156451

[5] Katvonik ,V. and Stankovic, L., in: Instantaneous frequency estimation using the Wigner distribution with varying and data-driven widow length, edited by IEEE Transactions on Signal Processing, 46(9), 2315-2325(1998).

DOI: 10.1109/78.709514

[6] Huang N.E., Shen,Z., Long,S., Wu,M., Shih,H., Zheng,Q., Yen,N., Tung,C., Liu,H., in: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, edited by Proceeding of the Royal Society of London Series A, 454, 903–995(1998).

DOI: 10.1098/rspa.1998.0193

[7] Huang N.E., Shen, Z., and Long, S. R., in: A new view of nonlinear water waves: the Hilbert Spectrum, edited by Annu. Rev. Fluid Mech. 31, 417–457(1999).

DOI: 10.1146/annurev.fluid.31.1.417

[8] Cheng, S.J., Yu, D.J., Yang,Y., in: The application of the energy operator demodulation approach based on EMD in machinery fault diagnosis, edited by Mechanical Systems and Signal Processing, 21: 668-677(2007, ).

DOI: 10.1016/j.ymssp.2005.10.005

[9] Peng, Z. K., Chu, F. L., in: A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing, edited by Mechanical Systems and Signal Processing, 19, 974–988(2005).

DOI: 10.1016/j.ymssp.2004.01.006

[10] Flandrin, P., Rilling,G., Goncalves, P., in: On empirical mode decomposition as a filter bank, edited by IEEE Signal Processing Letters, 11 (2)(2004).

DOI: 10.1109/lsp.2003.821662