  An extreme-point symmetric mode decomposition (ESMD) method is proposed to
improve the Hilbert-Huang Transform (HHT) through the following prospects: (1)
The sifting process is implemented by the aid of 1, 2, 3 or more inner
interpolating curves, which classifies the methods into ESMD_I, ESMD_II,
ESMD_III, and so on; (2) The last residual is defined as an optimal curve
possessing a certain number of extreme points, instead of general trend with at
most one extreme point, which allows the optimal sifting times and
decompositions; (3) The extreme-point symmetry is applied instead of the
envelop symmetry; (4) The data-based direct interpolating approach is developed
to compute the instantaneous frequency and amplitude. One advantage of the ESMD
method is to determine an optimal global mean curve in an adaptive way which is
better than the common least-square method and running-mean approach; another
one is to determine the instantaneous frequency and amplitude in a direct way
which is better than the Hilbert-spectrum method. These will improve the
adaptive analysis of the data from atmospheric and oceanic sciences,
informatics, economics, ecology, medicine, seismology, and so on.
