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Sensitivity of the Empirical Mode Decomposition to Interpolation Methodology and Data Non-stationarity

  • F. M. Z. BahriEmail author
  • J. J. Sharples
Article

Abstract

Empirical mode decomposition (EMD) is a commonly used method in environmental science to study environmental variability in specific time period. Empirical mode decomposition is a sifting process that aims to decompose non-stationary and non-linear data into their embedded modes based on the local extrema. The local extrema are connected by interpolation. The results of EMD strongly impact the environmental assessment and decision making. In this paper, the sensitivity of EMD to different interpolation methods, linear, cubic, and smoothing-spline, is examined. A range of non-stationary data, including linear, quadratic, Gaussian, and logarithmic trends as well as noise, is used to investigate the method’s sensitivity to different types of non-stationarity. The EMD method is found to be sensitive to the type of non-stationarity of the input data, and to the interpolation method in recovering low-frequency signals. Smoothing-spline interpolation gave overall the best. The accuracy of the method is also limited by the type of non-stationarity: if the data have an abrupt change in amplitude or a large change in the variance, the EMD method cannot sift correctly.

Keywords

Empirical mode decomposition Non-linear and non-stationary data Time series analysis Sensitivity analysis Interpolation method 

Notes

Acknowledgements

The authors would like to thank Peter McIntyre for help with proof reading. The authors are also indebted to the anonymous reviewer whose comments resulted in a significantly improved version of the manuscript.

Funding Information

This study was financially supported by the School of Physical, Environmental and Mathematical Sciences at the University of New South Wales Canberra.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of ScienceUNSW CanberraCanberraAustralia

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