Mean of values before and after a specific element

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I have an array of 1 row and 400 colmun, where all elments values are above 1500. However, I have some elements that have values <50 which are wrong measures and I would like to have the mean of the elments before and after the wrong measured data points and replace it in the main array
For intance, element number 17 is below 50 so I want to take the mean of elment 16 and 18 and replace element 17 with the new mean.
Can someone help me please.

Accepted Answer

Mohammad Sami
Mohammad Sami on 29 Jun 2021
Edited: Mohammad Sami on 29 Jun 2021
If you have Matlab version greater then R2017a, you can use filloutliers or you can replace the invalid values with NaN an then use fillmissing. You can use "linear" method to replace the ouliers / missing values, which will essentially take the mean of neighbouring values to fill in the outlier / missing value.
% generate test data
a = randi([1000 2000],1,400);
a(randperm(400,10)) = randi([1 50],1,10);
i = a<= 50;
b = filloutliers(a,'linear','mean');
colormap jet;
c = a;
c(i) = NaN;
c = fillmissing(c,'linear');
colormap jet;
  1 Comment
ennes mulla
ennes mulla on 29 Jun 2021
It worked!!! thanks a lot buddy! I truly appreciate your help

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More Answers (1)

Mathieu NOE
Mathieu NOE on 29 Jun 2021
hello see code below :
% dummy data
x = 1:1:400;
y = 1500 + 50*rand(1,400);
y(40)=35; % first outlier (single value)
y(60:66)=45; % second outliers (multiple values)
yi = y; % keep trace of initial y data (for plot)
all_idx = 1:length(x);
% outlier_idx = abs(y - median(y)) > 2*std(y); % Find outlier idx
outlier_idx = y<50; % Find outlier idx
y(outlier_idx) = []; % remove outlier from dataset
y = interp1(all_idx(~outlier_idx), y, x); % replace outliers by interpolated data


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