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How to get equation of signal in matlab

Asked by Muhammad Usman Saleem on 15 Jul 2017
Latest activity Commented on by Star Strider
on 24 Jul 2017
I want to find equation of this signal.
I plot this time series data and filter it with sSgolay filter. Now I want to get equation of this signal. Is this possible in matlab??
I have attached data of this plot with this post

  4 Comments

Show 1 older comment
You can get an equation for a neural network that models the I/O relationship.
HOWEVER, I don't see what good it will do
Greg
@Grej Thanks for your reply. I have not expertise in neural network. So I not know whether this network has created for such meteorological problems. In matlab I know there are some models like AR, ARMA which simulate the data set then forecast it. I tried them alot but unable to fit them due to lacking in expertise.
How can I forecast snow for one day ahead using this time series data set?
Thanks
help NARNET
doc NARNET
help NNCORR
doc NNCORR
greg NARNET
Hope this helps.
Greg

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1 Answer

Answer by Star Strider
on 15 Jul 2017

I am not certain what you intend by getting an equation for it.
You can filter out the noise to see the general trend:
fidi = fopen('data.txt','rt');
D = textscan(fidi, '%s%f', 'Delimiter','\t', 'CollectOutput',1);
t = datenum(D{1});
s = D{2};
Ts = mean(diff(t)); % Sampling Interval
Fs = 1/Ts; % Sampling Frequency
Fn = Fs/2; % Nyquist Frequency
L = numel(t); % Signal Length
sm = s-mean(s); % Subtract Mean To Make Amplitudes At Frequencies>0 More Prominent
FTs = fft(sm)/L; % Fourier Transform
Fv = linspace(0, 1, fix(L/2)+1)*Fn; % Frequency Vector
Iv = 1:length(Fv); % Index Vector
Phs = angle(FTs);
figure(1)
plot(Fv, abs(FTs(Iv))*2)
grid
xlabel('Frequency (Days^{-1})')
ylabel('Amplitude')
set(gca, 'XLim',[0 0.05])
Wp = [0.0045]/Fn; % Passband Frequencies (Normalised)
Ws = [0.0055]/Fn; % Stopband Frequencies (Normalised)
Rp = 10; % Passband Ripple (dB)
Rs = 50; % Stopband Ripple (dB)
[n,Ws] = cheb2ord(Wp,Ws,Rp,Rs); % Filter Order
[z,p,k] = cheby2(n,Rs,Ws); % Filter Design
[sosbp,gbp] = zp2sos(z,p,k); % Convert To Second-Order-Section For Stability
figure(2)
freqz(sosbp, 2^16, Fs) % Filter Bode Plot
s_filt = filtfilt(sosbp,gbp, s); % Filter Signal
figure(3)
plot(t, s, '-b')
hold on
plot(t, s_filt, '-r', 'LineWidth',1.5)
hold off
xlabel('Time (Days)')
ylabel('Amplitude')
legend('Original', 'Lowpass Filtered')

  8 Comments

@Muhammad Usman Saleem —
The point of my comment is that the neural network did not work.
I encourage you either to thoroughly study meteorological models and their complexity, or to abandon this idea and consider a more tractable problem.
Thank you sir so much!
As always, my pleasure!
If my Answer helped solve your problem, please Accept it.

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