how to remove outliers in large data sets?
3 visualizaciones (últimos 30 días)
Mostrar comentarios más antiguos
MUKESH KUMAR
el 7 de En. de 2022
Comentada: Image Analyst
el 17 de En. de 2022
I am unable to open example code of outliers (openExample('matlab/RemoveOutliersInVectorExample') ) and openExample('matlab/DetermineOutliersWithStandardDeviationExample') also.
I had large datasets of power load for three years at 30min interval, I want to remove the outliers poitns which is affecting my forecasting error.
any help to remove the outliers in such datasets would be appreciated.
Thanks
Reference image is attached which shows the outliers datasets in upper side of image, reference to these points the error is also high (as lower side of image).
Thanks again
2 comentarios
Image Analyst
el 7 de En. de 2022
How large is the data set? How many gigabytes? Can you attach a smaller set (less than 5 MB) in a .zip file?
Respuesta aceptada
Image Analyst
el 8 de En. de 2022
Try this:
data = readmatrix('Copy of data.xlsx');
x = data(:, 1);
y = data(:, 2);
% Plot just the first cycle.
last = round(70000/3)
x = x(1:last);
y = y(1:last);
subplot(2, 1, 1);
plot(x, y, 'b-')
grid on;
title('Showing One Cycle Only')
% Smooth the data.
windowWidth = 2001; % Some large odd number.
smoothY = movmean(y, windowWidth);
hold on;
plot(x, smoothY, 'r-', 'LineWidth', 3)
% Compute difference between actual and smoothed.
diffy = y - smoothY;
subplot(2, 1, 2);
plot(x, diffy, 'b-');
grid on;
% Detect outliers as having a MAD of more than 900
outlierIndexes = abs(diffy) > 900;
% Plot outliers as red dots over the original data.
subplot(2, 1, 1);
hold on
plot(x(outlierIndexes), y(outlierIndexes), 'r.', 'MarkerSize', 7);
% Now remove outliers from x and y
x(outlierIndexes) = [];
y(outlierIndexes) = [];
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/857065/image.png)
2 comentarios
Image Analyst
el 17 de En. de 2022
You must have a version so old that rmoutliers was not in it yet. However you can do it manually. Just smooth the curve and subtract it from your data and threshold like I did. I didn't use rmoutliers.
Más respuestas (0)
Ver también
Categorías
Más información sobre Data Distribution Plots en Help Center y File Exchange.
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!