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Using Empirical Mode Decomposition to Estimate Amplitudes in Noisy Data

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  • 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.

Suggested Citation

  • Claire Blackman, 2009. "Using Empirical Mode Decomposition to Estimate Amplitudes in Noisy Data," Royal Holloway, University of London: Discussion Papers in Economics 09/06, Department of Economics, Royal Holloway University of London.
    • Handle: RePEc:hol:holodi:0906
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    More about this item

    Keywords

    Empirical mode decomposition; amplitude estimation; low signal-to-noise data;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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