T. Alexandrov, S. Bianconcini, E. B. Dagum, P. Maass, and T. Mcelroy, A Review of Some Modern Approaches to the Problem of Trend Extraction, Statistics #2008-3, Statistical Research Division, 2008.
DOI : 10.1623/hysj.49.1.21.53996

P. Borgnat, P. Abry, P. Flandrin, and J. Rouquier, Studying Lyon's Vélo'v: A statistical cyclic model, Proceedings of ECCS'09 (European Conference of Complex Systems, 2009.

C. Chatfield, The analysis of time series: An introduction, 1996.

P. Flandrin and P. Gonçalves, Empirical mode decompositions as datadriven wavelet-like expansions, Multiresolution and Information Processing, pp.477-496, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00570611

P. Flandrin, P. Gonçalves, and G. Rilling, Detrending and denoising with empirical mode decompositions, Proceedings of EUSIPCO-04, pp.1581-1584, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00570614

P. Flandrin, G. Rilling, and P. Gonçalves, Empirical Mode Decomposition as a Filter Bank, IEEE Signal Processing Letters, vol.11, issue.2, pp.112-114, 2004.
DOI : 10.1109/LSP.2003.821662

URL : https://hal.archives-ouvertes.fr/inria-00570615

M. Ghil and R. Vautard, Interdecadal oscillations and the warming trend in global temperature time series, Nature, vol.350, issue.6316, pp.95-126, 1992.
DOI : 10.1038/350324a0

R. Henderson, Note on graduation by adjusted average, Transactions on the Actuarial Society of America, vol.17, pp.43-48, 1916.

R. J. Hodrick and E. C. Prescott, Postwar U.S. Business Cycles, Journal of Money, Credit, and Banking, vol.29, issue.1, pp.1-16, 1997.
DOI : 10.4324/9780203070710.pt8

N. E. Huang, Z. Shen, S. R. Long, M. L. Wu, H. H. Shih et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, pp.903-995, 1998.
DOI : 10.1098/rspa.1998.0193

N. E. Huang, M. Wu, S. Long, S. Shen, W. Qu et al., A confidence limit for the empirical mode decomposition and Hilbert spectral analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.459, issue.2037, pp.2317-2345, 2003.
DOI : 10.1098/rspa.2003.1123

D. S. Pollock, WIENER???KOLMOGOROV FILTERING, FREQUENCY-SELECTIVE FILTERING, AND POLYNOMIAL REGRESSION, Econometric Theory, vol.23, issue.01, pp.71-83, 2006.
DOI : 10.1016/S0304-4076(00)00028-2

G. Rilling, P. Flandrin, and P. Gonçalves, Empirical mode decomposition, fractional Gaussian noise, and Hurst exponent estimation, IEEE International Conference on Acoustics, Speech, and Signal Processing, pp.489-492, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00570581

R. Vautard and M. Ghil, Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series, Physica D: Nonlinear Phenomena, vol.35, issue.3, pp.395-424, 1989.
DOI : 10.1016/0167-2789(89)90077-8

R. Vautard, P. Yiou, and M. Ghil, Singular-spectrum analysis: A toolkit for short, noisy chaotic signals, Physica D: Nonlinear Phenomena, vol.58, issue.1-4, pp.324-327, 1991.
DOI : 10.1016/0167-2789(92)90103-T

Z. Wu and N. E. Huang, A study of the characteristics of white noise using the empirical mode decomposition method, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.460, issue.2046, pp.1597-1611, 2004.
DOI : 10.1098/rspa.2003.1221

Z. Wu, N. E. Huang, S. Long, and C. Peng, On the trend, detrending, and variability of nonlinear and nonstationary time series, Proceedings of the National Academy of Sciences, vol.104, issue.38, pp.14889-14894, 2007.
DOI : 10.1073/pnas.0701020104

. Appendix, EMD Trend Filtering for Multiplicative Mixes If the mix is multiplicative and the elements of C are positive, then the situation reduces to the additive case. Indeed, one can take logarithms to obtain log |X | = log C +log |Y|, where the logarithm and absolute value functions are being applied elementwise. The main question arising is whether the properties regarding the energy and