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.
Visualize the input data compared to the smoothed data.
Open the Task
To add the Smooth Data task to a live script in the MATLAB Live Editor:
On the Live Editor tab, click Task and select the Smooth Data icon
.
In a code block in the live script, type a relevant keyword, such as
smooth
ornoisy
. SelectSmooth Data
from the suggested command completions.
Examples
Parameters
Input data
— Valid input data from workspace
vector | table | timetable
This task operates on input data contained in a vector, table, or timetable. The
data can be of type single
, double
,
logical
, or signed or unsigned integer types such as
int64
.
For table or timetable input data, to smooth all variables with a supported type,
select All supported variables
. To smooth all variables of
type single
or double
, or signed or unsigned
integer types, select All numeric variables
. To choose which
supported variables to smooth, select Specified
variables
.
Smoothing method
— Method for smoothing data
Moving mean
(default) | Moving median
| Gaussian filter
| ...
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 (Lowess) | Lowess linear regression. This method can be computationally expensive, but it results in fewer discontinuities. |
Local quadratic regression (Loess) | Loess quadratic regression. This method is slightly more computationally expensive than local linear regression. |
Robust Lowess | Robust Lowess linear regression. This method is a more computationally expensive version of local linear regression, but it is more robust to outliers. |
Robust Loess | Robust Loess 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.
Version History
Introduced in R2019bSee Also
Functions
Live Editor Tasks
- Clean Missing Data | Clean Outlier Data | Find Change Points | Find Local Extrema | Remove Trends | Normalize Data | Compute by Group