Time Series Preprocessing and Forecasting Based on EMD

Abstract:

In this paper we pay attention to the preprocessing of time series and its application. We apply Empirical Mode Decomposition (EMD) to decompose three kinds of series into their components in order to study the data and forecast more efficiently. We try to unite EMD analysis and autoregressive integrated moving average processes (ARIMA) into a new forecasting technique which we call EMD-ARIMA. We find that our method is extraordinarily close to the original data.

• Periodical: Applied Mechanics and Materials (Volumes 313-314)
• Pages: 1256-1261
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.313-314.1256
• Online since: March 2013
• Authors: Guo Chen Feng, Peng Jian Shang, Xue Jiao Wang
• Keywords: Autoregressive Integrated Moving Average Processes (ARIMA), Detrended Fluctuation Analysis (DFA), Empirical Mode Decomposition (EMD)

References

[1] Norden E Huang, Zheng Shen, Steven R Long, Manli C wu, Hsing H(1998), The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Nonstationary Time Series Analysis, Proceedings of the Royal Society of London, v. 454, 903-995.

DOI: 10.1098/rspa.1998.0193

[2] Huang W, Shen Z, Norden E Huang, and Fung YC (1998). Engineering analysis of biological variables: an example of blood pressure over 1 day. Proc. Natl. Acad. Sci. USA, (95):4816–4821.

DOI: 10.1073/pnas.95.9.4816

[3] Norden E Huang, Zheng Shen, Steven R Long(1999), A new view of nonlinear water waves: the hilbert spectrum, Annu. Rev. Fluid Mech, (31):417–457.

DOI: 10.1146/annurev.fluid.31.1.417

[4] Norden E Huang, Chern C C, Huang K, et al(2001), A New Spectral Representation of Earthquake Data: Hilbert Spectral Analysis of Station, Bulletin Of The Seismological Society Of America, 91(5): 1310-1338.

DOI: 10.1785/0120000735

[5] Louis Yu Lu(2008). Fast Intrinsic Mode Decomposition and Filtering of Time Series Data. Arxiv preprint arXiv:0808.2827.

[6] Amir Bashan, Ronny Bartsch, Jan W Kantelhardt, et al(2008), Comparison of detrending methods for fluctuation analysis, Physica A, 387：5080-5090.

DOI: 10.1016/j.physa.2008.04.023

[7] C-K Peng, S V Buldyrev, S Havlin, M Simons, H E Stanley, A L Goldberger(1994), Mosaic organization of DNA nucleotides, Phys. Rev. E 49,1685.

DOI: 10.1103/physreve.49.1685

[8] George E P Box，Gwilym M Jenkins，Gregory C Reinsel(1970), Time Series :Analysis Forecasting And Control, New Jersey: Prentice Hall.

[9] Jan W Kantelhardt, Stephan A Zschiegner, Eva Koscielny-Bunde, et al(2002). Multifractal detrended fluctuation analysis of nonstationary time series[J]. Physica A, 316:87-114.

DOI: 10.1016/s0378-4371(02)01383-3

[10] Kun Hu, Plamen Ch Ivanov, Zhi Chen, et al(2001), Effect of trends on detrended fluctuation analysis.