Smooth Data

Smooth noisy data in the Live Editor

Description

The Smooth Data task 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.

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Open the Task

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

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

• In a code block in the script, type a relevant keyword, such as smooth or noisy. Select Smooth 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

vector | table | timetable | ...

This task operates on data of type single, double, or logical, or signed or unsigned integer types such as int64. The data can be contained in a vector or table variables. When providing a table or timetable for the input data, specify All supported variables to operate on all variables with a supported type. Choose All numeric variables to operate on all variables of type single or double, or signed or unsigned integer types. To choose specific supported variables to operate on, select Specified variables and then select the variables individually.

Smoothing method — Method for smoothing data

Moving mean (default) | Moving median | Gaussian filter | Local linear regression | Local quadratic regression | Robust local linear regression | Robust local quadratic regression | Savitzky-Golay polynomial filter | ...

Specify the smoothing method as one of the following 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.

Moving window — Window for smoothing methods

Centered (default) | Asymmetric

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 the X-axis variable units.