|Subject:||Re: [Kriging-help] machine learning - multiple target variables|
|Date:||Mon, 15 Dec 2014 09:27:56 +0100|
|User-agent:||Mozilla/5.0 (X11; Linux i686; rv:31.0) Gecko/20100101 Thunderbird/31.3.0|
Le 08/12/2014 18:22, Lazar A. a écrit :
Thank you so much for you interest in STK.
*** I also forward your question to the help-octave mailing list, where other people might have a different opinion. ***
Your problem, as I understand it, can be seen as a space-time modeling problem.
Assuming, e.g., that each of you elevation point is describes by its (x,y) position on a map, you can consider your input dataset as a set of
n = 1095 x 69 = 75555
evaluations point on a factor space of dimension
d = 3 + 2 = 5
(three inputs that you already had, plus two additional inputs for the space coordinates).
STK could *in principle* help you with that, provided that you can come up with an appropriate space-time covariance function. (There is a very rich literature about that, but an anisotropic Matérn covariance function, as already provided in STK, could be used for a start.)
Unfortunately, the current state of implementation of STK cannot handle such large datasets (n = 75555).
If you want to help us improve STK to deal with such large datasets (there are several possible approaches proposed in the literature), you're welcome to join the project !
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