Difference between revisions of "Extrapolation"
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(Created page with "Extrapolation is often required when interpolation techniques cannot be used while mapping information from one dataset to a grid. Extrapolation methods vary, yielding different ...") |
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==Weighted Average== | ==Weighted Average== | ||
| − | <math>O = \frac{ \sum_i | + | Suppose you have a discrete dataset <math>I</math>, with coordinates <math>X</math> on a normed space, 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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| + | <math>O_k = \frac{ \sum_i w_{ki} \, I_i }{\sum_i w_{ki}}; </math> | ||
==Geometric Weighted Average== | ==Geometric Weighted Average== | ||
Revision as of 11:27, 10 February 2012
Extrapolation is often required when interpolation techniques cannot be used while 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 based that use all the information available to extrapolate. The techniques inspire from the weighted average principle.
Contents
Weighted Average
Suppose you have a discrete dataset 
, with coordinates 
on a normed space, and want to map the dataset to another dataset, 
, with a new set of coordinates 
. The operation is performed by constructing mapping coefficients 
, also called "weights", and by performing a linear combination of the elements of with the weights, , to compute each element of .
