Curve Fitting Toolbox

Fit curves and surfaces to data using regression, interpolation, and smoothing

Curve Fitting Toolbox provides an app and functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, preprocess and post-process data, compare candidate models, and remove outliers. You can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations. The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modeling techniques, such as splines, interpolation, and smoothing.

After creating a fit, you can apply a variety of post-processing methods for plotting, interpolation, and extrapolation; estimating confidence intervals; and calculating integrals and derivatives.

What Is Curve Fitting Toolbox?

Curve Fitter App

Interactively fit data to curves and surfaces, visualize plots, and understand fitting statistics using the Curve Fitter app. Explore various fitting methods and options through the app and generate MATLAB code for reusability and automation.

Documentation | Examples

Screenshot of the Curve Fitter app for fitting a custom non-linear equation with the fit plot and residuals plot.

Regression

Model a continuous response variable as a function of predictors using linear and nonlinear regression techniques, including custom equations with adequate options to optimize solver parameters and starting conditions to improve fit quality.

Documentation | Examples

Plot comparing the nearest neighbor and PCHIP interpolant fits.

Interpolation

Estimate values between known data points using Interpolation techniques. Extrapolate values outside the fitting data domain for interpolant curves and surfaces.

Documentation | Examples

Plot of a smooth surface that highlights the differences between model and table data in investigating fuel efficiency.

Smoothing

Reduce noise and remove seasonal trends in the data set by applying smoothing techniques and other methods such as moving average, Savitzky-Golay filter, and Lowess models or by fitting a smoothing spline.

Documentation | Examples

A plot of points in 3D space connected by a cubic spline curve.

Splines

Fit various splines to data, including cubic and smoothing splines with various end conditions, for curves, surfaces, and higher dimensional objects. Control advanced spline operations, including break/knot manipulation, optimal knot placement, and data-point weighting.

Documentation | Examples

Graphing fitted curves and prediction bounds.

Fit Analysis and Export to Simulink

Analyze the fitted model by exploring and customizing plots, estimating confidence intervals, and calculating integrals and derivatives. Export fitted models as Simulink Lookup table blocks or fitted objects.

Documentation | Examples

Product Resources:

Documentation Examples Videos Technical articles Functions Requirements Release notes

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