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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 theCurve Fitter应用程序或在命令行上我们ing thesmoothfunction. For an example showing how to smooth data, seeFit Smooth Surfaces to Investigate Fuel Efficiency.

Apps

Curve Fitter Fit curves and surfaces to data

Functions

datastats Data statistics
excludedata Exclude data from fit
fit Fit curve or surface to data
fittype Fit type for curve and surface fitting
fitoptions Create or modify fit options object
get Get fit options structure property names and values
set Assign values in fit options structure
smooth 平滑的响应数据
prepareCurveData Prepare data inputs for curve fitting
prepareSurfaceData Prepare data inputs for surface fitting

Topics

  • Smoothing Splines

    Fit smoothing splines in the Curve Fitter app or with thefitfunction 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 thefitfunction using Lowess models.

  • Filtering and Smoothing Data

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

  • 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.