We address the problem of capturing and tracking local
correlations among time evolving time series. Our approach
is based on comparing the local auto-covariance
matrices (via their spectral decompositions) of each series
and generalizes the notion of linear cross-correlation. In
this way, it is possible to concisely capture a wide variety
of local patterns or trends. Our method produces a general
similarity score, which evolves over time, and accurately
reflects the changing relationships. Finally, it can
also be estimated incrementally, in a streaming setting. We
demonstrate its usefulness, robustness and efficiency on a
wide range of real datasets.