Curve Fitting Toolbox


Curve Fitting Toolbox

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

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.


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.


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


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.


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.

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.

“MATLAB enabled us, as geologists, to use our expertise in predictive frameworks, analytics, and analog matching to implement algorithms that are unique in our industry. With the help of MathWorks consultants, we then deployed those algorithms as an easy-to-use application to our colleagues worldwide.”

Nick Howes, Shell

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