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Trend Filtering via Empirical Mode Decompositions

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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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Dates and versions

ensl-00565293 , version 1 (11-02-2011)
ensl-00565293 , version 2 (15-02-2011)

Identifiers

  • HAL Id : ensl-00565293 , version 2

Cite

Azadeh Moghtaderi, Patrick Flandrin, Pierre Borgnat. Trend Filtering via Empirical Mode Decompositions. 2011. ⟨ensl-00565293v2⟩
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