I have a series a of values (0 and 1) coming from a Brownian process with drift for which I am studying the autocorrelation.
I used two methods:
1) numpy autocorrelation:
corr = np.correlate(a,a,mode='full')/a.size corr = corr[corr.size//2:]
2) Fourier transforms (Wiener-Khinchin):
A = np.fft.fft(a) S = np.conj(A)*A/a.size c_fourier = np.fft.ifft(S)
However, I get different results:
I am not very experienced with stochastics or signal processing, so I have some difficulties understanding where the difference comes from.