Functions > Data Analysis > Interpolation and Prediction > About Interpolation and Prediction Functions
Interpolation is the process of finding intermediate values in data by fitting appropriate functions (typically polynomials) piece-wise through adjacent data points. This method contrasts with regression, in which a single function is fit, in a least-squares sense, through all data points.
Since interpolation functions must pass through all data points, they are very sensitive to spurious data. If your data is noisy, consider using a regression function instead.
interp—Interpolation at a given point of the output of cspline, lspline, pspline, bspline or loess
bspline, Spline2, Binterp, DWS—B-spline interpolation with user-supplied knots
cspline, pspline, lspline, Bicubic2D—Cubic spline interpolation in one and two dimensions, with cubic, parabolic, and linear end conditions
linterp—Linear interpolation
predict—Linear prediction
polyint, polycoeff, polyinter—Polynomial interpolation
rationalint—Rational function interpolation
Thielecoeff, Thiele—Thiele continued fraction interpolation