Using Empirical Mode Decomposition to Estimate Amplitudes in Noisy Data

Claire Blackman
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Claire Blackman: Department of Economics, Royal Holloway, University of London

No 09/06, Royal Holloway, University of London: Discussion Papers in Economics from 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.

Keywords: Empirical mode decomposition; amplitude estimation; low signal-to-noise data (search for similar items in EconPapers)
JEL-codes: C02 C15 C63 (search for similar items in EconPapers)
Date: 2009-05
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