Trend filtering via empirical mode decompositions
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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Volume (Year): 58 (2013)
Issue (Month): C ()
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- Hodrick, Robert J & Prescott, Edward C, 1997.
"Postwar U.S. Business Cycles: An Empirical Investigation,"
Journal of Money, Credit and Banking,
Blackwell Publishing, vol. 29(1), pages 1-16, February.
- Robert J. Hodrick & Edward Prescott, 1981. "Post-War U.S. Business Cycles: An Empirical Investigation," Discussion Papers 451, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Beran, Jan & Feng, Yuanhua, 2002. "SEMIFAR models--a semiparametric approach to modelling trends, long-range dependence and nonstationarity," Computational Statistics & Data Analysis, Elsevier, vol. 40(2), pages 393-419, August.
- Maravall, A. & del Rio, A., 2007.
"Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter,"
Computational Statistics & Data Analysis,
Elsevier, vol. 52(2), pages 975-998, October.
- Agustín Maravall & Ana del Río, 2007. "Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter," Working Papers 0728, Banco de España;Working Papers Homepage.