method: which algorithm to use to generate surrogate data
num_surr: the number of null surrogates to generate
T_period: the period of seasonality for seasonal surrogates (ignored for other methods)
alpha: additive noise factor: N(0,alpha)
Returns
A matrix where each column is a separate surrogate with the same length as ts.
Details
Method "random_shuffle" creates surrogates by randomly permuting the values of the original time series.
Method "Ebisuzaki" creates surrogates by randomizing the phases of a Fourier transform, preserving the power spectra of the null surrogates.
Method "seasonal" creates surrogates by computing a mean seasonal trend of the specified period and shuffling the residuals. It is presumed that the seasonal trend can be exracted with a smoothing spline. Additive Gaussian noise is included according to N(0,alpha).