Trend filtering via empirical mode decompositions
AbstractThe 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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Bibliographic InfoArticle provided by Elsevier in its journal Computational Statistics & Data Analysis.
Volume (Year): 58 (2013)
Issue (Month): C ()
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Web page: http://www.elsevier.com/locate/csda
Empirical mode decomposition; Trend filtering; Adaptive data analysis; Monthly mean carbon dioxide cycle; Seasonality;
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- Alejandra Carmona & Germán Poveda, 2014. "Detection of long-term trends in monthly hydro-climatic series of Colombia through Empirical Mode Decomposition," Climatic Change, Springer, vol. 123(2), pages 301-313, March.
- Bangzhu Zhu & Ping Wang & Julien Chevallier & Yiming Wei, 2014. "Carbon price analysis using empirical mode decomposition," Working Papers 2014-156, Department of Research, Ipag Business School.
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