Hello, have you ever heard of EMD, EEMD?
Empirical Mode Decomposition (EMD)
This decomposition provides a powerful method to look into the different processes behind a given time series, and provides a way to separate short time-scale events from a general trend.
The fundamental part of the HHT is the empirical mode decomposition (EMD) method. Breaking down signals into various components, EMD can be compared with other analysis methods such as Fourier transform and Wavelet transform. Using the EMD method, any complicated data set can be decomposed into a finite and often small number of components. These components form a complete and nearly orthogonal basis for the original signal. In addition, they can be described as intrinsic mode functions (IMF).
Because the first IMF usually carries the most oscillating (high-frequency) components, it can be rejected to remove high-frequency components (e.g., random noise). EMD based smoothing algorithms have been widely used in seismic data processing, where high-quality seismic records are highly demanded.
Without leaving the time domain, EMD is adaptive and highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it can be applied to nonlinear and nonstationary processes.
Ensemble Empirical Mode Decomposition (EEMD), a noise assisted version of the EMD algorithm. The EEMD works by adding a certain amplitude of white noise to a time series, decomposing it via EMD, and saving the result. If this is done enough times, the averages of the noise perturbed IMFs will approach the ``true'' IMF set. The EEMD can ameliorate mode mixing and intermittency problems
libeemd – a C library for performing the ensemble empirical mode decomposition.
EEMD works better for me, it would be great to have it in Zorro.
Is there a chance that it gets implemented or what do you think is the best way to use it in Zorro - without using R?