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Trend filtering via empirical mode decompositions

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  • Moghtaderi, Azadeh
  • Flandrin, Patrick
  • Borgnat, Pierre
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    Abstract

    The problem of filtering low-frequency trend from a given time series is considered. In order to solve this problem, a nonparametric technique called empirical mode decomposition trend filtering is developed. A key assumption is that the trend is representable as the sum of intrinsic mode functions produced by the empirical mode decomposition (EMD) of the time series. Based on an empirical analysis of the EMD, an automatic procedure for selecting the requisite intrinsic mode functions is proposed. To illustrate the effectiveness of the technique, it is applied 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 bicycle rental service (rental numbers of Grand Lyon Vélo’v).

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    File URL: http://www.sciencedirect.com/science/article/pii/S0167947311001812
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    Bibliographic Info

    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 58 (2013)
    Issue (Month): C ()
    Pages: 114-126

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    Handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:114-126

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    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords: Empirical mode decomposition; Trend filtering; Adaptive data analysis; Monthly mean carbon dioxide cycle; Seasonality;

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