I am analyzing a group of related time-series variables, and am wondering the best way to think of in-group correlations. For example, suppose that my population is daily leaf-fall for all trees. The subgroup of all Quercus Nigra trees in Whateverton, Georgia are all very highly correlated in the sense that, on any given day, if the leaf-fall rate of one tree is high, one could expect the leaf-fall rate of other trees in that group to be high. On the other hand, the group of all trees is not (or at least less) correlated, since if the leaf-fall rate of one Quercus Nigra tree in Whateverton is high, we cannot expect the leaf-fall rate of an evergreen tree in South America (or another arbitrary tree) to be high.
Between two individual time-series I can use a cross correlation. What would be the best way to generalize this to the concept of in-group correlation?
Also, what are other classic and new ways to measure/think about this sort of correlation. Where can I read more about it?
Best Answer
Hierarchical Clustering sounds like what you are looking for. There are packages for R and Python that you can use. You can do it with time series by using appropriate distance metrics and appropriate ways of combining the distances. The result is, depending on the metrics you choose, a set of groups where correlations within the group are higher than correlations between groups.