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Smooth Data

Smooth noisy data in the Live Editor

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Description

TheSmooth Datatask lets you interactively smooth noisy data. The task automatically generates MATLAB®code for your live script.

Using this task, you can:

  • Customize the method for smoothing data in a workspace variable.

  • Adjust parameters to generate less or more smoothing.

  • Automatically visualize the smoothed data.

Smooth Data task in Live Editor

Open the Task

To add theSmooth Datatask to a live script in the MATLAB Live Editor:

  • On theLive Editortab, click任务and select theSmooth Dataicon.

  • In a code block in the live script, type a relevant keyword, such assmoothornoisy. SelectSmooth Datafrom the suggested command completions.

Examples

Parameters

This task operates on input data contained in a vector, table or timetable. The data can be of typesingle,double,logical, or signed or unsigned integer types such asint64.

When providing a table or timetable for the input data, specifyAll supported variablesto operate on all variables with a supported type. ChooseAll numeric variablesto operate on all variables of typesingleordouble, or signed or unsigned integer types. To choose specific supported variables to operate on, selectSpecified variablesand then select the variables individually.

Specify the smoothing method as one of these options, which operate over local windows of data.

Method Description
Moving mean

Moving average. This method is useful for reducing periodic trends in data.
Moving median Moving median. This method is useful for reducing periodic trends in data when outliers are present.
Gaussian filter Gaussian-weighted moving average.
Local linear regression Linear regression. This method can be computationally expensive, but it results in fewer discontinuities.
Local quadratic regression Quadratic regression. This method is slightly more computationally expensive than local linear regression.
Robust local linear regression Robust linear regression. This method is a more computationally expensive version of local linear regression, but it is more robust to outliers.
Robust local quadratic regression Robust quadratic regression. This method is a more computationally expensive version of local quadratic regression, but it is more robust to outliers.
Savitzky-Golay polynomial filter Savitzky-Golay polynomial filter, which smooths according to a polynomial of specified degree, and is fitted over each window. This method can be more effective than other methods when the data varies rapidly.

Specify the window type and size for the smoothing method instead of specifying a general smoothing factor.

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 theX-axisvariable units.

Version History

Introduced in R2019b

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Behavior changed in R2022a

See Also

Functions

Live Editor Tasks

Apps