I would like to create a low-pass filter to remove outliers from a time series.
They are sea surface temperature (tsm) data, in this series there are some data that are outliers and do not know very well how to create a low-pass filter, could anyone help me?
NOTE: The tsm data is in vector form. The sampling frequency I think is 5 minutes.
I'm starting to mess with Matlab now and I'm not sure how to do it.
Thank you.
There are different techniques for different purposes, but in general the removal of Outliers are identified and removed from a vector in the following way:
X = 1:100;
X = X';
noise = randn(100,1);
noise2 = 10*randn(100,1);
noise(11:20:91) = noise2(11:20:91);
Y = 3*X + 2 + noise;
stats = regstats(Y,X,'linear');
potential_outlier = stats.cookd > 4/length(X);
X(potential_outlier)
scatter(X,Y, 'b.')
hold on
scatter(X(potential_outlier),Y(potential_outlier), 'r.')
You can check this code better Here with explanations