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Smoothing

Fit using smoothing splines and localized regression, smooth data with moving average and other filters

Smoothing is a method of reducing the noise within a data set. Curve Fitting Toolbox™ allows you to smooth data using methods such as moving average, Savitzky-Golay filter and Lowess models or by fitting a smoothing spline.

Smooth data interactively using the Curve Fitter app or at the command line using the smooth function. For an example showing how to smooth data, see Fit Smooth Surfaces to Investigate Fuel Efficiency.

Apps

Curve FitterFit curves and surfaces to data

Functions

datastatsData statistics
excludedataExclude data from fit
fitFit curve or surface to data
fittypeFit type for curve and surface fitting
fitoptionsCreate or modify fit options object
getGet fit options structure property names and values
setAssign values in fit options structure
smoothSmooth response data
prepareCurveData Prepare data inputs for curve fitting
prepareSurfaceDataPrepare data inputs for surface fitting

Topics

  • Smoothing Splines

    Fit smoothing splines in the Curve Fitter app or with the fit function to create a smooth curve through data and specify the smoothness.

  • Lowess Smoothing

    Fit smooth surfaces to your data in the Curve Fitter app or with the fit function using Lowess models.

  • Filtering and Smoothing Data

    Use the smooth function to smooth response data, using methods for moving average, Savitzky-Golay filters, and local regression with and without weights and robustness (lowess, loess, rlowess and rloess).

  • Fit Smooth Surfaces to Investigate Fuel Efficiency

    This example shows how to use Curve Fitting Toolbox™ to fit a response surface to some automotive data to investigate fuel efficiency.

  • Nonparametric Fitting

    Perform nonparametric fitting to create smooth curves or surfaces through your data with interpolants and smoothing splines.

Featured Examples