Curve Fitting Toolbox

Smoothing

Smoothing algorithms are widely used to remove noise from a data set while preserving important patterns. Curve Fitting Toolbox supports both smoothing splines and localized regression, which enable you to generate a predictive model without specifying a functional relationship between the variables.

Localized regression model. Smoothing techniques can be used to generate predictive models without specifying a parametric relationship between the variables.

 Nonparametric Fitting 4:08
Develop a predictive model when you can’t specify a function that describes the relationship between variables.

Curve Fitting Toolbox supports localized regression using either a first-order polynomial (lowess) or a second-order polynomial (loess). The toolbox also provides options for robust localized regression to accommodate outliers in the data set. Curve Fitting Toolbox also supports moving average smoothers such as Savitzky-Golay filters.

Exploratory data analysis using a Savitzky-Golay filter. Smoothing data enables you to identify periodic components.

Learn more about smoothing