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Linear extrapolation means creating a tangent line at the end of the known data and extending it beyond that limit. Linear extrapolation will only provide good results when used to extend the graph of an approximately linear function or not too far beyond the known data.
Linear interpolation on a data set (red points) consists of pieces of linear interpolants (blue lines). Linear interpolation on a set of data points (x 0, y 0), (x 1, y 1), ..., (x n, y n) is defined as piecewise linear, resulting from the concatenation of linear segment interpolants between each pair of data points.
Prediction outside this range of the data is known as extrapolation. Performing extrapolation relies strongly on the regression assumptions. The further the extrapolation goes outside the data, the more room there is for the model to fail due to differences between the assumptions and the sample data or the true values.
Linear predictive analysis is a simple form of first-order extrapolation: if it has been changing at this rate then it will probably continue to change at approximately the same rate, at least in the short term. [1] This is equivalent to fitting a tangent to the graph and extending the line. [2]
An example of MUSCL type left and right state linear-extrapolation. MUSCL based numerical schemes extend the idea of using a linear piecewise approximation to each cell by using slope limited left and right extrapolated states. This results in the following high resolution, TVD discretisation scheme,
While the interpolation formula can be found by solving a linear system of equations, there is a loss of intuition in what the formula is showing and why Newton's interpolation formula works is not readily apparent. To begin, we will need to establish two facts first: Fact 1.
The map X is linear and it is a projection on the subspace () of polynomials of degree n or less. The Lebesgue constant L is defined as the operator norm of X. One has (a special case of Lebesgue's lemma): ‖ ‖ (+) ‖ ‖.
Nearest neighbor interpolation (blue lines) in one dimension on a (uniform) dataset (red points) Nearest neighbor interpolation on a uniform 2D grid (black points). Each colored cell indicates the area in which all the points have the black point in the cell as their nearest black point.