Remove a part of data and replace it with data to fit trend

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VISHNURAJ SREERAJ NAIR
评论： Hiro Yoshino 2022-1-11
Hello, can some one please help me with the removal of data in the photo. I tried all the outlier removals, smooth data and conventional funtions, but it is unable to detect.

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回答（1 个）

Hiro Yoshino
Hiro Yoshino 2022-1-10
Try this. It removes the dent you see in the plot. Please note this also affect the both edges of the profile.
[cleanedData,outlierIndices,thresholdLow,thresholdHigh] = filloutliers(slip1,...
"linear","movmean",29000,"ThresholdFactor",1,"SamplePoints",T1);
It would also be a good idea to detrend first and then find the outliers by using isoutlier.
  1 个评论
Hiro Yoshino
Hiro Yoshino 2022-1-11
I found a better solution.
  1. decompose the profile into long-term, short-term and roughness profiles.
  2. detect outliers in roughness and replace them with other values (linear interpolation in this case)
  3. re-compose the new roughness back to the long and short profiles together
Here's the code:
[LT, ST, R] = trenddecomp(slip1);
plot(T1,LT,T1,ST,T1,R);
[Rnew,outlierIndices,thresholdLow,thresholdHigh] = filloutliers(R,...
"linear","quartiles","SamplePoints",T1);
slip1New = Rnew + LT + ST;
plot(T1,spring1,T1,slip1New);

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