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)
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https://hal-ens-lyon.archives-ouvertes.fr/ensl-00565293
Contributor : Pierre Borgnat <>
Submitted on : Friday, February 11, 2011 - 3:48:54 PM
Last modification on : Thursday, April 19, 2018 - 2:54:03 PM
Long-term archiving on : Thursday, May 12, 2011 - 2:51:02 AM

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  • HAL Id : ensl-00565293, version 1

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

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