I am trying to iterate through a set of samples that seems to show periodic changes. I need continuously apply the fit function to get the fourier series coefficients, the regression has to be n samples in the past (in my case, around 30). The problem is, my code is extremely slow! It will take like 1 hour to do this for a set of 50,000 samples. Is there any way to optimize this? What am I doing wrong?
Here's my code:
function[coefnames,coef] = fourier_regression(vect_waves,n)
j = 1;
coef = zeros(length(vect_waves)-n,10);
for i=n+1:length(vect_waves)
take_fourier = vect_waves(i-n+1:i);
x = 1:n;
f = fit(x,take_fourier,'fourier4');
current_coef = coeffvalues(f);
coef(j,1:length(current_coef)) = current_coef;
j = j + 1;
end
coefnames = coeffnames(f);
end
When I call [coefnames,coef] = fourier_regression(VECTOR,30);
This takes forever to compute. Is there any way to fix it? What's wrong with my code?
Note: I have a intel i7 5500 U cpu, 16GB RAM, and using Matlab 2015a.