Smooth noisy data in the Live Editor
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.
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
noisy. Select Smooth
Data from the suggested command completions.
Input data— Valid input data from workspace
This task operates on data of type
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
double, or signed
or unsigned integer types. To choose specific supported variables to operate on, select
Specified variables and then select the variables
Smoothing method— Method for smoothing data
Moving mean(default) |
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.
Moving average. This method is useful for reducing periodic trends in data.
|Moving median. This method is useful for reducing periodic trends in data when outliers are present.|
|Gaussian-weighted moving average.|
|Linear regression. This method can be computationally expensive, but it results in fewer discontinuities.|
|Quadratic regression. This method is slightly more computationally expensive than 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 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, 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
Specify the window type and size for the smoothing method instead of specifying a general smoothing factor.
|Specified window length centered about the current point.|
|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.