I have about 15 weather stations, separated by quite a bit of kilometers. The data in these stations are the same for all, so is the resolution (daily).
I want to try and find which of the stations are actually correlated enough to perform some kind of predictive modelling. So in short, I would select a weather station and extract its values, and use those values in conjunction with nearby weather stations' values (for which they have strong correlation) to forecast the values for the selected weather station.
But that's the big picture here. And predictive modelling with ANN is already covered.
But how does one test if two or more locations are spatially correlated with regards to one of their weather variables?
Thanks to anyone who replies.
The data (actual readings from stations) is a time series ranging from 2000 - 2016, this exists per station. So think of it like 15 different time series (what's the plural for series?). Variables range from average temps, wind speed, predicpitation, humidity and so on and so forth. (Precipitation here being the variable I want to forecast.)
At this stage, I want to test spatial correlation. But this part is just exploratory analysis, I just want to know which locations are correlated enough to consider. I won't be using all the stations, since that would just drive the Artificial Neural Network (ANN) insane. Will build the ANN later, but for now stations first.
I'm a programmer not a statistician. If you guys have software that can do this automatically plus some documentation for its use and/or the actual math for it, that would be infinitely better.
EDIT 3 The space in question is an island about 104,530 km^2 in area.
My goal is basically just this, forecast the precipitation values of one station using the values from its history and values from history taken at other nearby (at least correlated in some modicum of strength) stations.
Forecast not predict. As was suggested, I changed terminology here. Problem stays the same though. And by that I want to forecast week ahead values for the station in question (aka. 7 days).