Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Trend Filtering via Empirical Mode Decompositions

Abstract : The present work is concerned with the problem of extracting low-frequency trend from a given time series. To solve this problem, the authors develop a nonparametric technique called empirical mode decomposition (EMD) trend filtering. A key assumption is that the trend is representable as the sum of intrinsic mode functions produced by the EMD. Based on an empirical analysis of the EMD, the authors propose an automatic procedure for selecting the requisite intrinsic mode functions. To illustrate the effectiveness of the technique, the authors apply it to simulated time series containing different types of trend, as well as real-world data collected from an environmental study (atmospheric carbon dioxide levels at Mauna Loa Observatory) and from a large-scale bicycle rental service (rental numbers of Grand Lyon Vélo'v)
Complete list of metadata

https://hal-ens-lyon.archives-ouvertes.fr/ensl-00565293
Contributor : Pierre Borgnat Connect in order to contact the contributor
Submitted on : Friday, February 11, 2011 - 3:48:54 PM
Last modification on : Saturday, March 27, 2021 - 7:26:02 AM
Long-term archiving on: : Thursday, May 12, 2011 - 2:51:02 AM

File

Revision_CSDA-D-10-00417.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : ensl-00565293, version 1

Collections

Citation

Azadeh Moghtaderi, Patrick Flandrin, Pierre Borgnat. Trend Filtering via Empirical Mode Decompositions. 2011. ⟨ensl-00565293v1⟩

Share

Metrics

Record views

47

Files downloads

90