Time Series processing using fft I have a set of real data (timestamp and value) with an unstable step between samples (5sec, 30sec etc.).
The data is the % of fillage of a vehicle's tank through time.
Due to the harsh volatility of the data, I need to perform fft to transfer my time series to the frequency domain, select a cutoff point to remove all the noise and then transfer back to the time domain.
Is my thought process correct?
I want to use matlab but I cannot figure out what I have to do with my data. The first 10 rows (I have a document with 10ths of thousands of data) are as follows:

I have produced some dummy timestamp data with the same values and with time step at 0.01sec.
After writing the code in matlab I get the following:


My code is:
load input.txt;
plot(input);
figure;
Fs = 1/0.01;
Ts = 1/Fs;
dt = 0:Ts:5-Ts;
x = input(:,1);
y = input(:,2);
nfft = length(y);
nfft2 = 2*nextpow2(nfft);
ff = fft(y,nfft2);
fff = ff(1:nfft2/2);ff
plot(abs(fff));

If you want some data don't hesitate to contact me.
Thank you in advance!
Is my thought process correct?

No. Frequency domain filtering is difficult and even if you get it right, I sincerely doubt that low-pass filtering will solve your problem
Your data is very noisy. I looks like there are two noise sources: one with a &quot;quantization&quot; step of about 20 plus some smaller noise overlaid. Hard to tell without further analysis.
You haven't told us yet, what you actually want to get out of the analysis, so it's hard to give advise. Low-pass filtering will smear out your &quot;refuel&quot; transients a lot. I'm guessing, your best shot would be to build a parametric model and do a piece-wise least squares error fit, but again, you need to tell us what you actually want to achieve.