Similarity between time series signals Lets say I have 2 time series that look like this for a number of different test subjects:

The lines represent values on 2 different metrics recorded at different points in time (minutes)
I have a prediction that says when the blue signal is high, the green will be low, and vice versa (i.e. they are inversely related). You can see this prediction play out between time points 1-7
I'm looking for a way to quantify the level of similarity (or I guess dissimilarity in this case) between the two signals, maybe some inferential test, to suggest that my prediction/hypothesis is correct. Are there any techniques designed for this type of analysis?
I've seen a few posts on here suggesting the use of a cross-correlation (e.g. Correlation between two time series), however, I dont think that would be useful in this case. A cross-correlation will tell me the maximum degree of similarity, and also the lag at which that similarity occurs. But I'm not really interested in similarity after time shifting because I only care about similarity as the data is recorded (lag 0)
I've tried a regular pearson correlation, but its resulting in really small correlation values. Are there any other methods that can help me determine whether my hypothesis is correct?
 A: First considering for a single subject. If we take each of the time points as independent, we should be able to state the hypothesis that for the equation 
$$ y = \beta_1 + \beta_2x $$ 
$ H_0: \beta_2 = 0 $
$ H_1: \beta_2 < 0 $
Fitting an equation to find our estimate for $\beta_2$ and the standard error in our estimate $se(\beta_2)$ we can find the $t$ statistic from 
$$ t = \frac{\beta_2}{se(\beta_2)} $$
The $t$ statistic can then be compared to tables of $t$ statistics. 
For the case with many different subjects, it could depend on whether we need to take factors to do with the subjects out of the the problem, or whether we can bundle all of the data points into the model above.
Extensive reference here http://www.uv.es/uriel/4%20Hypothesis%20testing%20in%20the%20multiple%20regression%20model.pdf from page 5 onwards. 
I hope I have understood the question correctly. 
A: the simple cross-correlation doesn't make sense for two reasons 1) auto-correlation and 2) embedded/latent pulses/level shifts/seasonal pulses /local time trends thus it is possible to compute a robust cross-correlation using data adjusted for the embedded structure.
See my answer to How to quantify the effect of outliers when estimating a regression coefficient?
