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:

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.

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.

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.