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Using Empirical Mode Decomposition to Estimate Amplitudes in Noisy Data

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Author Info
Claire Blackman () (Department of Economics, Royal Holloway, University of London)
Abstract

Empirical Mode Decomposition, an adaptive data-driven technique which can be used to extract non-stationary signals buried in noise, seldom admits theoretical calculation of the statistical properties of the extracted signals. Instead, numerical experiments are required. In this paper we use Monte Carlo simulations to investigate the accuracy of the amplitudes of sinusoids extracted from synthetic noisy signals using Empirical Mode Decompo- sition. We show that even for relatively low signal-to-noise data, the amplitude of the extracted signal is close to true amplitude. We also show that edge effects due to the spline curves which are used to calculate the decom- position do not affect the amplitude estimate beyond the first two oscillations.

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File URL: http://www.rhul.ac.uk/economics/Research/WorkingPapers/pdf/dpe0906.pdf
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Publisher Info
Paper provided by Department of Economics, Royal Holloway University of London in its series Royal Holloway, University of London: Discussion Papers in Economics with number 09/06.

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Date of creation: May 2009
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Handle: RePEc:hol:holodi:0906

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Postal: Egham Hill, Egham, Surrey, TW20 0EX, UK.
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Web page: http://www.rhul.ac.uk/economics/

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Postal: Egham Hill, Egham, Surrey, TW20 0EX, UK.
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Related research
Keywords: Empirical mode decomposition; amplitude estimation; low signal-to-noise data;

Find related papers by JEL classification:
C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

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This page was last updated on 2009-11-5.


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