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Noise Reduction for Nonlinear Nonstationary Time Series Data using Averaging Intrinsic Mode Function
Centre for Bio-Inspired Technology, Imperial College London, South Kensington, London SW7 2AZ, UK
Ubon Rachathani Rajabhati Univeristy, 2 Ratchathani Road, Ubon Ratchathani 34000, Thailand
* Author to whom correspondence should be addressed.
Received: 28 May 2013; in revised form: 13 June 2013 / Accepted: 4 July 2013 / Published: 19 July 2013
Abstract: A novel noise filtering algorithm based on averaging Intrinsic Mode Function (aIMF), which is a derivation of Empirical Mode Decomposition (EMD), is proposed to remove white-Gaussian noise of foreign currency exchange rates that are nonlinear nonstationary times series signals. Noise patterns with different amplitudes and frequencies were randomly mixed into the five exchange rates. A number of filters, namely; Extended Kalman Filter (EKF), Wavelet Transform (WT), Particle Filter (PF) and the averaging Intrinsic Mode Function (aIMF) algorithm were used to compare filtering and smoothing performance. The aIMF algorithm demonstrated high noise reduction among the performance of these filters.
Keywords: empirical mode decomposition; Intrinsic Mode Function; Wavelet Transform; noise reduction; exchanges rates
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Cite This Article
MDPI and ACS Style
Premanode, B.; Vongprasert, J.; Toumazou, C. Noise Reduction for Nonlinear Nonstationary Time Series Data using Averaging Intrinsic Mode Function. Algorithms 2013, 6, 407-429.
Premanode B, Vongprasert J, Toumazou C. Noise Reduction for Nonlinear Nonstationary Time Series Data using Averaging Intrinsic Mode Function. Algorithms. 2013; 6(3):407-429.
Premanode, Bhusana; Vongprasert, Jumlong; Toumazou, Christofer. 2013. "Noise Reduction for Nonlinear Nonstationary Time Series Data using Averaging Intrinsic Mode Function." Algorithms 6, no. 3: 407-429.