Empirical Mode Decomposition : Improvement and Application

Empirical mode decomposition (EMD), a relatively new form of time series decomposition, has the feature of not assuming a time series is linear or stationary (like Fourier analysis). In hydroclimatology, where most variables exhibit non-linear and non-stationary behaviour, this feature is particularly useful, allowing more meaningful quantification of the proportion of variance in a time series due to fluctuations at different time scales than previous spectral analysis techniques. However, in its original form the EMD algorithm relies on cubic spline interpolation, which is suspected of inflating the variance of the resultant Intrinsic Mode Functions (IMFs) and residual (trend). In this paper a potential improvement to the EMD algorithm is briefly outlined and its effect on an example is assessed.