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Suppose, I have 7 timeseries, that represent a changing value measured in a point with coordinates x,y. Also I have a group of 20 points distributed spatially within the coverage of these 7 points. Thus I want to get 20 time series, where every value is an interpolated value of initial 7 points on a corresponding moment. Timestep is day. I know that kriging is the best interpolation method in my case. Also I know that kriging interpolation of several points over a regular grid is easy to perform with scikit-learn or pykrige packages. But I want time series (time cycle? wouldnt it work too slowly?) and I want irregular points as target positions of interpolated values, not a regular grid.

So, what is the optimal decision here?

I've seen this theme but there is no time cycling.

On the scheme are shown: points with measured time-series (x,y,value) as o and points which are targets for interpolation as x. scheme of points

Nikolay Yasinskiy
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  • do you have to interpolate over time, too, or just over the position, but for every time step? – J. Coenen Oct 12 '18 at 09:19
  • Just over position, only the result is to be timeseries (interpolation performed for each timestep for all points). It would be better if the method admits also a possibility of regression analisys in future(now I would like to interpolate only, by position) – Nikolay Yasinskiy Oct 12 '18 at 09:32

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