Interpolation is a technique for adding new data points within a range of a set of known data points. You can use interpolation to fill-in missing data, smooth existing data, make predictions, and more. Interpolation in MATLAB® is divided into techniques for data points on a grid and scattered data points.
1-D and Gridded Interpolation
|1-D data interpolation (table lookup)
|Interpolation for 2-D gridded data in meshgrid format
|Interpolation for 3-D gridded data in meshgrid format
|Interpolation for 1-D, 2-D, 3-D, and N-D gridded data in ndgrid format
|Gridded data interpolation
|Piecewise Cubic Hermite Interpolating Polynomial (PCHIP)
|Modified Akima piecewise cubic Hermite interpolation (Since R2019b)
|Cubic spline data interpolation
|Evaluate piecewise polynomial
|Make piecewise polynomial
|Extract piecewise polynomial details
|Padé approximation of time delays
|1-D interpolation (FFT method)
- Gridded and Scattered Sample Data
Introduction to interpolating gridded and scattered data sets.
- Interpolating Gridded Data
Interpolation of regularly spaced, axis-aligned data sets.
- Interpolating Scattered Data
Interpolating scattered data using