Clean Outlier Data

Find, fill, or remove outliers in the Live Editor

Description

The Clean Outlier Data task lets you interactively handle outliers in data. The task automatically generates MATLAB^® code for your live script.

Using this task, you can:

• Find, fill, or remove outliers from data in a workspace variable.

• Customize the methods for finding and filling outliers.

• Automatically visualize the outlier data and cleaned data.

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Open the Task

To add the Clean Outlier Data task to a live script in the MATLAB Editor:

• On the Live Editor tab, select Task > Clean Outlier Data.

• In a code block in the script, type a relevant keyword, such as outlier or clean. Select Clean Outlier Data from the suggested command completions.

Examples

Related Examples

• Add Interactive Tasks to a Live Script
• Clean Messy Data and Locate Extrema Using Live Editor Tasks
• Data Smoothing and Outlier Detection

Parameters

Input data — Valid input data from workspace

numeric vector | table | timetable | ...

This task operates on data of type single or double contained in a vector or table variables. When providing a table or timetable for the input data, specify All supported variables to clean all variables with type single or double, or choose which single or double variables to clean by selecting Specified variables and then selecting the variables individually.

Cleaning method — Cleaning method for filling outliers

Linear interpolation (default) | Constant value | Center value | Clip to threshold value | Previous value | Next value | Nearest value | Spline interpolation | Shape-preserving cubic interpolation (PCHIP) | Modified Akima cubic interpolation | ...

Specify the method for filling outliers using one of the following options.

Fill Method	Description
Linear interpolation	Linear interpolation of neighboring, nonoutlier values.
Constant value	Specified scalar value, which is 0 by default.
Center value	Center value determined by the find method.
Clip to threshold value	Fills lower threshold value for elements smaller than the lower threshold determined by the find method. Fills with the upper threshold value for elements larger than the upper threshold determined by the find method.
Previous value	Previous nonoutlier value.
Next value	Next nonoutlier value.
Nearest value	Nearest nonoutlier value.
Spline interpolation	Piecewise cubic spline interpolation.
Shape-preserving cubic interpolation (PCHIP)	Shape-preserving piecewise cubic spline interpolation.
Modified Akima cubic interpolation	Modified Akima cubic Hermite interpolation.

Detection method — Method for detecting outliers

Moving median (default) | Median | Mean | Quartiles | Grubbs | Generalized extreme studentized deviate (GESD) | Moving mean | Percentiles | ...

Specify the detection method for finding outliers using one of the following options.

Method	Description
Moving median	Outliers are defined as elements more than the specified threshold of local scaled MAD from the local median over a specified window. The default threshold is 3.
Median	Outliers are defined as elements more than the specified threshold of scaled median absolute deviations (MAD) from the median, which is 3 by default. For input data A, the scaled MAD is defined as c*median(abs(A-median(A))), where c=-1/(sqrt(2)*erfcinv(3/2)).
Mean	Outliers are defined as elements more than the specified threshold of standard deviations from the mean, which is 3 by default. This method is faster but less robust than Median.
Quartiles	Outliers are defined as elements more than the specified threshold of interquartile ranges above the upper quartile (75 percent) or below the lower quartile (25 percent), which is 1.5 by default. This method is useful when the input data is not normally distributed.
Grubbs	Outliers are detected using Grubbs’s test, which removes one outlier per iteration based on hypothesis testing. This method assumes that the input data is normally distributed.
Generalized extreme studentized deviate (GESD)	Outliers are detected using the generalized extreme studentized deviate test for outliers. This iterative method is similar to Grubbs, but can perform better when multiple outliers are masking each other.
Moving mean	Outliers are defined as elements more than the specified threshold of local standard deviations from the local mean over a specified window. The default threshold is 3.
Percentiles	Outliers are defined as elements outside of the percentile range specified by an upper and lower threshold. The default lower percentile threshold is 10 and the default upper percentile threshold is 90. Valid threshold values are in the interval [0,100].

Moving window — Window for moving methods

Centered (default) | Asymmetric

Specify the window type and size when the method for detecting outliers is Moving median or Moving mean.

Window	Description
Centered	Specified window length centered about the current point.
Asymmetric	Specified window containing the number of elements before the current point and the number of elements after the current point.

Window sizes are relative to the X-axis variable units.