In general, comparing time series is problematic, both because it's hard to find a good divergence measure, and, then, once such a measure is in hand, it in general is itself correlated in time. Time series can differ in phase as well as amplitudes, even if they are standardized. The most comprehensive treatment of such comparisons comes, I believe, from the seminal work of Sugihara and May in 1990, described and applied thereafter in a series of papers by their students. Began with G. Sugihara, R. M. May, "Nonlinear forecasting as a way of distinguishing between chaos and measurement error in time series", _Nature_, 344(6268), 734-741, 1990, and http://dx.doi.org/10.1098/rsta.1994.0106, and ended up in things like http://dx.doi.org/10.1126/science.1227079, and http://dx.doi.org/10.1890/14-1479.1 (also http://www.esajournals.org/doi/pdf/10.1890/14-1479.1), http://dx.doi.org/10.1371/journal.pone.0018295, and even http://www.pnas.org/content/96/25/14210.full.pdf. There's also software available to do this, per https://cran.r-project.org/web/packages/multispatialCCM/multispatialCCM.pdf.
_Updated_ _14th_ _September_ _2015_: Maher and Hernandez (2015), CauseMap: fast inference of causality from complex time series. PeerJ 3:e824;
DOI [10.7717/peerj.824](http://dx.doi.org/10.7717/peerj.824). There is also a [pre-publication review of the same](https://publons.com/review/78979/).
It has also come to my attention that [Corey Chivers](http://bayesianbiologist.com/), from whom I learn a lot, has an _R_ [version in the works](https://github.com/cjbayesian/rccm) and that there [is a Python version](https://pypi.python.org/pypi/pyccm).