How can I detect and remove outliers from a large dataset?

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Arinze 2014-3-12

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评论： Nivodi 2018-8-14

plot.jpg

I am presently trying to process a large dataset (n = 5000000) and I am really facing challenges writing codes that could detect and remove all the outliers present in the dataset. I tried using modified thomson tau method but it didnt work and I am presently trying to apply modified z- score method but still cant make a head go with the matlab codes.

Attached is the plot of the signal with peaks and dips for better understanding. I also want to fill the deleted outlier points with an interpolation and would appreciate a suggestion.

Please, I will appreciate any further assistance on how to get rid of the peaks and dips on the signal and how to fill the removed outliers points with an interpolation.

I will appreciate any suggestions on other methods to use to remove the outliers and if possible codes for the method.

thank you.

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Star Strider 2014-3-12

Do you have any trends in your data that you could model, perhaps with nlinfit or other regression routines? I have no idea what you are doing or what your data are, but detecting trends and other patterns first could make your task easier.

Arinze 2014-3-15

Star Strider, I attached a picture of the plot for better understanding.

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Answer 1

Shahab B 2016-9-30

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How can I used it for simple data such as: main=[0 347.666506871168 97.948966303887 98.8584847142621 96.4002074686564];

note that the outlier data is = 347.666506871168 .

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Image Analyst 2016-9-30

在 MATLAB Online 中打开

Try this

clc;
clearvars;
close all;
workspace;
fontSize = 30;
vector = [0 347.666506871168 97.948966303887 98.8584847142621 96.4002074686564];
%---------------------------------------------------------------
% This won't work.  Not enough data to determine percentiles.
percntiles = prctile(vector,[5 95]) %5th and 95th percentile
outlierIndexes = vector < percntiles(1) | vector > percntiles(2);
% Extract outlier values:
outliers = vector(outlierIndexes)
% Extract non-outlier values:
nonOutliers = vector(~outlierIndexes)
%---------------------------------------------------------------
% This will work.
% Compute the median absolute difference
meanValue = mean(vector)
% Compute the absolute differences.  It will be a vector.
absoluteDeviation = abs(vector - meanValue)
% Compute the median of the absolute differences
mad = median(absoluteDeviation)
% Find outliers.  They're outliers if the absolute difference
% is more than some factor times the mad value.
sensitivityFactor = 6 % Whatever you want.
thresholdValue = sensitivityFactor * mad;
outlierIndexes = abs(absoluteDeviation) > thresholdValue
% Extract outlier values:
outliers = vector(outlierIndexes)
% Extract non-outlier values:
nonOutliers = vector(~outlierIndexes)
%---------------------------------------------------------------
% Fancy plots in the following section.  Delete if you don't need it.
% Show the original data and the absolute deviation
subplot(2, 1, 1);
bar(vector);
hold on;
line(xlim, [meanValue, meanValue], 'Color', 'r', 'LineWidth', 2);
grid on;
title('Original Data', 'FontSize', fontSize);
message = sprintf('Mean Value = %.2f', meanValue);
text(3, 150, message, 'FontSize', 18, 'Color', 'r');
subplot(2, 1, 2);
bar(absoluteDeviation);
grid on;
title('Absolute Deviations', 'FontSize', fontSize);
% Put a line for the mad.
line(xlim, [mad, mad], 'Color', 'r', 'LineWidth', 2);
message = sprintf('MAD Value = %.2f', mad);
text(3, 50, message, 'FontSize', 18, 'Color', 'r');
% Put a line for the mad.
line(xlim, [thresholdValue, thresholdValue], 'Color', 'r', 'LineWidth', 2);
message = sprintf('Outlier Threshold Value = %.2f', thresholdValue);
text(3, 200, message, 'FontSize', 18, 'Color', 'r');
% Set up figure properties:
% Enlarge figure to full screen.
set(gcf, 'Units', 'Normalized', 'OuterPosition', [0 0 1 1]);
% Get rid of tool bar and pulldown menus that are along top of figure.
set(gcf, 'Toolbar', 'none', 'Menu', 'none');
% Give a name to the title bar.
set(gcf, 'Name', 'Demo by ImageAnalyst', 'NumberTitle', 'Off')

Image Analyst 2016-11-21

There are several definitions of MAD. My code above does definition 1.2.1 as listed on this page https://en.wikipedia.org/wiki/Average_absolute_deviation which gives 4 definitions using all combinations of mean and median. You're welcome to use whichever of those definitions best meets your needs.

Nivodi 2018-8-14

在 MATLAB Online 中打开

Image Analyst, how can I apply this part of your code to several columns?

% Compute the median absolute difference
meanValue = mean(vector)
% Compute the absolute differences.  It will be a vector.
absoluteDeviation = abs(vector - meanValue)
% Compute the median of the absolute differences
mad = median(absoluteDeviation)
% Find outliers.  They're outliers if the absolute difference
% is more than some factor times the mad value.
sensitivityFactor = 6 % Whatever you want.
thresholdValue = sensitivityFactor * mad;
outlierIndexes = abs(absoluteDeviation) > thresholdValue
% Extract outlier values:
outliers = vector(outlierIndexes)
% Extract non-outlier values:
nonOutliers = vector(~outlierIndexes)t% Compute the median absolute difference
meanValue = mean(vector)
% Compute the absolute differences.  It will be a vector.
absoluteDeviation = abs(vector - meanValue)
% Compute the median of the absolute differences
mad = median(absoluteDeviation)
% Find outliers.  They're outliers if the absolute difference
% is more than some factor times the mad value.
sensitivityFactor = 6 % Whatever you want.
thresholdValue = sensitivityFactor * mad;
outlierIndexes = abs(absoluteDeviation) > thresholdValue
% Extract outlier values:
outliers = vector(outlierIndexes)
% Extract non-outlier values:
nonOutliers = vector(~outlierIndexes)

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Answer 2

Image Analyst 2014-3-12

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median_absolute_deviation.m

That's not large. It's just a fraction of the size of a typical digital image. You can use "deleteoutliers" from Brett Shoelson of the Mathworks: http://www.mathworks.com/matlabcentral/fileexchange/3961-deleteoutliers Or you could try the Median Absolute Deviation (a popular statistical method for detecting outliers) as demonstrated on an image in the file I attached.

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Arinze 2014-3-15

编辑：Arinze 2014-3-15

plot1.jpg

new plot of the signal, Please my matlab doesnt recognise 'deleteoutliers' command, any idea why?

Image Analyst 2014-3-15

So is anything that is not exactly zero an outlier?

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Answer 3

Tim leonard 2014-3-12

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在 MATLAB Online 中打开

Trimming your values based on percentiles is quick and powerful -

    vector = randi(100,100,1);
    percntiles = prctile(vector,[5 95]); %5th and 95th percentile
    outlierIndex = vector < percntiles(1) | vector > percntiles(2);
    %remove outlier values
    vector(outlierIndex) = [];

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Image Analyst 2014-3-12

But something at the 1% or 99% or 100% percentile is not necessarily an outlier so you could be getting rid of good data. It's quick but I wouldn't call it powerful. I'd call it risky, unless you know for a fact that you have a certain specific amount of noise present.

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Answer 4

Amir H. Souri 2017-6-26

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Hi, I may be late, but I just want to point out that definition of outlier is totally subjective. In order to find them, you need to estimate the probably distribution of your data, and fit a distribution (say for example Gaussian), and check whether it is statistically significant (you may use Kolmogorov–Smirnov test or a bootstrap method). Then, you will be able to identify the outliers by defining the confidence interval. For example, you can say any data within 95% confidence interval are acceptable and others can be ignored as outliers. As I mentioned there is no absolute answer, and it totally depends on the nature of data and how strict you want to be in regards to the confidence interval.

Good luck!

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