6533b836fe1ef96bd12a1627
RESEARCH PRODUCT
Introducing libeemd: a program package for performing the ensemble empirical mode decomposition
Esa RäsänenJouni HelskeP. J. J. Luukkosubject
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; Detrendingdescription
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 decomposition. We release our implementation, libeemd, with the aim of providing a user-friendly, fast, stable, well-documented and easily extensible EEMD library for anyone interested in using (E)EMD in the analysis of time series data. While written in C for numerical efficiency, our implementation includes interfaces to the Python and R languages, and interfaces to other languages are straightforward.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2016-01-01 |