Uploaded August 2026 | Updated September 2026, 2 weeks ago
Spline fitting is an alternative to polynomial fitting for engineering and scientific data. When data is noisy, irregular, or densely sampled, fitting a single high-degree polynomial often leads to overfitting and wild oscillations between data points. Splines solve this by breaking the data into segments and fitting smooth, low-degree piecewise polynomials to each one.
This video covers two main approaches to spline fitting: interpolating splines, which pass through every data point exactly, and smoothing splines, which approximate the overall trend while filtering out noise. Understanding when to use each type is a practical decision that depends on the quality and characteristics of the data.
The video demonstrates spline fitting interactively using the Curve Fitter app in MATLAB® from the Curve Fitting Toolbox™. The app provides a visual, code-free workflow for fitting cubic splines, smoothing splines, and other spline types to data—with real-time visual feedback as the smoothing parameter is adjusted. The demonstration shows how to load data, select a spline fit type, compare results against polynomial fits, and evaluate the quality of the fit.
Topics covered include: what spline fitting is and how it differs from polynomial fitting, why splines avoid the overfitting problems common with high-degree polynomials, the difference between cubic spline interpolation and smoothing splines, and how to perform spline fitting using the Curve Fitter app without writing code.
Spline fitting and spline interpolation are widely used across engineering disciplines for data smoothing, signal processing, and curve approximation. MATLAB and Curve Fitting Toolbox provide both interactive and programmatic tools for constructing, evaluating, and comparing spline fits.
Related resources:
- Explore spline fitting documentation for Curve Fitting Toolbox: bit.ly/4wp9qs5
- Try the Curve Fitter app: bit.ly/3xFxIVe
- Read more about smoothing splines: bit.ly/4fDUekB
- What Is Curve Fitting Toolbox?: bit.ly/4fmVDuI
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Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2026 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Spline fitting is an alternative to polynomial fitting for engineering and scientific data. When data is noisy, irregular, or densely sampled, fitting a single high-degree polynomial often leads to overfitting and wild oscillations between data points. Splines solve this by breaking the data into segments and fitting smooth, low-degree piecewise polynomials to each one.
This video covers two main approaches to spline fitting: interpolating splines, which pass through every data point exactly, and smoothing splines, which approximate the overall trend while filtering out noise. Understanding when to use each type is a practical decision that depends on the quality and characteristics of the data.
The video demonstrates spline fitting interactively using the Curve Fitter app in MATLAB® from the Curve Fitting Toolbox™. The app provides a visual, code-free workflow for fitting cubic splines, smoothing splines, and other spline types to data—with real-time visual feedback as the smoothing parameter is adjusted. The demonstration shows how to load data, select a spline fit type, compare results against polynomial fits, and evaluate the quality of the fit.
Topics covered include: what spline fitting is and how it differs from polynomial fitting, why splines avoid the overfitting problems common with high-degree polynomials, the difference between cubic spline interpolation and smoothing splines, and how to perform spline fitting using the Curve Fitter app without writing code.
Spline fitting and spline interpolation are widely used across engineering disciplines for data smoothing, signal processing, and curve approximation. MATLAB and Curve Fitting Toolbox provide both interactive and programmatic tools for constructing, evaluating, and comparing spline fits.
Related resources:
- Explore spline fitting documentation for Curve Fitting Toolbox: bit.ly/4wp9qs5
- Try the Curve Fitter app: bit.ly/3xFxIVe
- Read more about smoothing splines: bit.ly/4fDUekB
- What Is Curve Fitting Toolbox?: bit.ly/4fmVDuI
--------------------------------------------------------------------------------------------------------
Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2026 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
