IAHR, founded in 1935, is a worldwide independent member-based organisation of engineers and water specialists working in fields related to the hydro-environmental sciences and their practical application. Activities range from river and maritime hydraulics to water resources development and eco-hydraulics, through to ice engineering, hydroinformatics, and hydraulic machinery.
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You are here : eLibrary : IAHR World Congress Proceedings : 34th Congress - Brisbane (2011) : THEME 1: Extremes and Variability : Ensemble empirical mode decomposition: testing and objective automation
Ensemble empirical mode decomposition: testing and objective automation
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Ensemble Empirical Mode Decomposition (EEMD), a recently developed improvement over traditional Empirical Mode Decomposition (EMD), utilises the concept of noise assisted data analysis to decompose a time series into Intrinsic Mode Functions (IMFs) and a residua (or trend). Because the EEMD algorithm is locally adaptive it is robust when applied to non stationary and non-linear data and is suitable for decomposing hydroclimatic time series that appear non-stationary in terms of mean and variance. Using synthetic time series constructed with fluctuations of known frequency and a range of trends (linear, ramp, step and parabolic), we report results from our investigation into the ability of EEMD to decompose the synthetic time series and reproduce the embedded known components. We discuss objective automation of the EEMD algorithm based on the synthetic time series analysis. EEMD is then applied to Southern Oscillation Index time series to demonstrate the potential for using EEMD for non-stationary stochastic data generation.
File Size : 583,280 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 34th Congress - Brisbane (2011)
Article : THEME 1: Extremes and Variability
Date Published : 01/07/2011
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