Finding the dominant frequency of a time series data using fft matlab

I'm trying to determine the dominant frequency of a time series data using the fft function in matlab. my data is represented as a vector while my time scale is also a vector. Below is my sample code:

Fs = 10; % sampling frequency 1 kHz
t =  [0,10,20,30,40,50,60,70,80,90]; % time scale
x = [10,120,130,120,120,100,123,456,78,89]; % time series
plot(t,x), axis('tight'), grid('on'), title('Time series'), figure
nfft = 512; % next larger power of 2
y = fft(x,nfft); % Fast Fourier Transform
y = abs(y.^2); % raw power spectrum density
y = y(1:1+nfft/2); % half-spectrum
[v,k] = max(y); % find maximum
f_scale = (0:nfft/2)* Fs/nfft; % frequency scale
plot(f_scale, y),axis('tight'),grid('on'),title('Dominant Frequency')
fest = f_scale(k); % dominant frequency estimate
fprintf('Dominant freq.: true %f Hz, estimated %f Hznn', f, fest)
fprintf('Frequency step (resolution) = %f Hznn', f_scale(2))

The problem is that my dominant frequency here is 0 which am not quite sure if it is correct. Could some provide feedback on this please especially if the Fs matters alot in this case!

[SANJAY KUMAR]

what is 'f'

[Walter Roberson]

@SANJAY KUMAR

f is additional information not shown here, that gives information about what the true frequency of the signal is.

Answer 1

[Star Strider]

The ‘frequency’ at 0 is the mean of your signal (or D-C offset). To eliminate it, subtract the mean before doing the fft.

This gives you the result you want:

Fs = 10; % sampling frequency 1 kHz
t =  [0,10,20,30,40,50,60,70,80,90]; % time scale
x = [10,120,130,120,120,100,123,456,78,89]; % time series
x = x - mean(x);                                            % <= ADDED LINE
plot(t,x), axis('tight'), grid('on'), title('Time series'), figure
nfft = 512; % next larger power of 2
y = fft(x,nfft); % Fast Fourier Transform
y = abs(y.^2); % raw power spectrum density
y = y(1:1+nfft/2); % half-spectrum
[v,k] = max(y); % find maximum
f_scale = (0:nfft/2)* Fs/nfft; % frequency scale
plot(f_scale, y),axis('tight'),grid('on'),title('Dominant Frequency')
fest = f_scale(k); % dominant frequency estimate
fprintf('Dominant freq.: true %f Hz, estimated %f Hznn\n', fest, fest)
fprintf('Frequency step (resolution) = %f Hznn\n', f_scale(2))

[David Gureya]

Thanks for the prompt response

[Star Strider]

My pleasure.

[Vanisa Syahra]

hello, I have the same problem with David, but here, eventhough I subtract the signal with the mean, I still get the zero frequency which has the highest power. Why this still happen? i am pretty sure that the dminant freq is not 0.

[Walter Roberson]

Is it possible that you have a 2D signal and that you are subtracting the mean along the wrong dimension?

Answer 2

[Eric Marshal]

hello mr. David Gureya, may i ask you how you get 512 as NFFT ? are the n(number) from time series is diffrent with NFFT ?