Calculate the similarity between pairs of time series data
Euclidean distance metric is unsuitable for time series...In short, it is invariant to time shifts, ignoring the time dimension of the data. If two time series are highly correlated, but one is shifted by even one time step, Euclidean distance would erroneously measure them as further apart. You might use it to compare your time series if you are extracting their features. Don't need to go and use directly PCA. You can use simple descriptive stats such as mean, min, max, kurtosis, etc...Then you can compare it with Euclidean.
I advise you to give it a try with DTW. ;)
How does it work?
http://alexminnaar.com/2014/04/16/Time-Series-Classification-and-Clustering-with-Python.html   (you can check that blog post, it explains it nicely)
Good luck to you!