Search results for "intrinsic mode function"

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Introducing libeemd: a program package for performing the ensemble empirical mode decomposition

2016

The ensemble empirical mode decomposition (EEMD) and its complete variant (CEEMDAN) are adaptive, noise-assisted data analysis methods that improve on the ordinary empirical mode decomposition (EMD). All these methods decompose possibly nonlinear and/or nonstationary time series data into a finite amount of components separated by instantaneous frequencies. This decomposition provides a powerful method to look into the different processes behind a given time series data, and provides a way to separate short time-scale events from a general trend. We present a free software implementation of EMD, EEMD and CEEMDAN and give an overview of the EMD methodology and the algorithms used in the deco…

Statistics and ProbabilityFOS: Computer and information sciences010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologies02 engineering and technology01 natural sciencesExtensibilityStatistics - ComputationHilbert–Huang transformSoftware implementationHilbert–Huang transformSannolikhetsteori och statistikTime seriesProbability Theory and StatisticsComputation (stat.CO)021101 geological & geomatics engineering0105 earth and related environmental sciencescomputer.programming_languagenoise-assisted data analysisintrinsic mode functionPython (programming language)adaptive data analysisComputational MathematicsNonlinear systemtime series analysisData analysisStatistics Probability and UncertaintyAlgorithmcomputerdetrendingHilbert-Huang transform; Intrinsic mode function; Time series analysis; Adaptive data analysis; Noise-assisted data analysis; Detrending
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