Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/empirical_mode_decomposition.R

This function implements the complete ensemble empirical mode decomposition (CEEMD) algorithm.

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`sig` |
a time series to be decomposed (vector) |

`tt` |
The sample times of |

`noise.amp` |
Amplitude of white noise to use in denoising algorithm |

`trials` |
Number of times to run EMD |

`verbose` |
If TRUE, notify when each trial is complete |

`spectral.method` |
See |

`diff.lag` |
See |

`tol` |
See |

`max.sift` |
See |

`stop.rule` |
See |

`boundary` |
See |

`sm` |
See |

`smlevels` |
See |

`spar` |
See |

`max.imf` |
See |

`interm` |
See |

`noise.type` |
If unspecified or |

`noise.array` |
If |

This function performs the complete ensemble empirical mode decomposition, a noise assisted empirical mode decomposition algorithm. The CEEMD works by adding a certain amplitude of white noise to a time series, decomposing it via EMD, and saving the result. In contrast to the Ensemble Empirical Mode Decomposition (EEMD) method, the CEEMD also ensures that the IMF set is quasi-complete and orthogonal. The CEEMD can ameliorate mode mixing and intermittency problems. Keep in mind that the CEEMD is a computationally expensive algorithm and may take significant time to run.

`ceemd.result` |
The final result of the CEEMD algorithm |

.

Daniel Bowman [email protected]

Torres, M. E., Colominas, M. A., Schlotthauer, G., Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise.
*2011 IEEE International Conference on Acoustics, Speech, and Signal Processing*, pp.4144-4147, doi: 10.1109/ICASSP.2011.5947265.

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hht documentation built on May 29, 2017, 9:26 a.m.

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