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Difference between revisions of "Extrapolation"

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==Weighted Average==
 
==Weighted Average==
Suppose you have a discrete dataset <math>I</math>, with coordinates <math>X</math> on a normed space, <math>d\left(\cdot,\,\cdot\right)</math>, and want to map the dataset to another dataset, <math>O</math>, with a new set of coordinates <math>Y</math>. The operation is performed by constructing mapping coefficients <math>w</math>, also called "weights", and by performing a linear combination of the elements of <math>I</math> with the weights, <math>w</math>, to compute each element of <math>O</math>.
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Suppose you have a discrete dataset <math>I</math>, with coordinates <math>X</math> on a normed space, <math>d\left(.\,,\,.\right)</math>, and want to map the dataset to another dataset, <math>O</math>, with a new set of coordinates <math>Y</math>. The operation is performed by constructing mapping coefficients <math>w</math>, also called "weights", and by performing a linear combination of the elements of <math>I</math> with the weights, <math>w</math>, to compute each element of <math>O</math>.
  
 
<math>O_k = \frac{ \sum_i w_{ki} \, I_i }{\sum_i w_{ki}}; </math>
 
<math>O_k = \frac{ \sum_i w_{ki} \, I_i }{\sum_i w_{ki}}; </math>

Revision as of 10:41, 10 February 2012

Extrapolation is often required when interpolation techniques cannot be used; often when mapping information from one dataset to a grid. Extrapolation methods vary, yielding different results both in quality and in performance. This article proposes a couple of simple extrapolation techniques that use all the information available to extrapolate. The techniques inspire from the weighted average principle.

Weighted Average

Suppose you have a discrete dataset I, with coordinates X on a normed space, d\left(.\,,\,.\right), and want to map the dataset to another dataset, O, with a new set of coordinates Y. The operation is performed by constructing mapping coefficients w, also called "weights", and by performing a linear combination of the elements of I with the weights, w, to compute each element of O.

O_k = \frac{ \sum_i w_{ki} \, I_i }{\sum_i w_{ki}};

Any method of constructing the weights is valid and depends on the nature of the mapping. A practical use-case consists in performing an interpolation/extrapolation of the data from I to O and thus the weights w are usually a function of the coordinates X and Y and of the norm of the space. More complex techniques could be used, with w also as a function of I and its derivatives, but these will not be considered in this text.

Geometric Weighted Average

Squared Weighted Average

Gaussian Weighted Average

See also