How to calculate percental change of two time series? I have the two sets of data;
$A = $    1.5010, 1.3118, 1.4219, 1.1650, 1.2720, 1.5421, 1.4211, 1.1832, 1.5378 1.4357, 1.2707, 1.2411, 1.4833, 1.5578
and
$B = $    1.4039, 1.1912, 1.3596, 1.4168, 1.6269, 1.4635, 1.2787, 1.5260, 1.8160, 1.6103, 1.5315, 1.7100, 1.5181, 1.4049
The data is meant to represent the amount of negative words people use on a discussion forum before and after a political election. The higher value, the more negative the forum post is. $A$ is two weeks before the event, and $B$ is two weeks after, where $A_7$ is day 7, for example.
So I made a two sampled paired t-test in R where $$H_0: \mu_{A} - \mu_{B} = 0$$ 
                                            $$H_a: \mu_{A} - \mu_{B} < 0$$ 
And I get a p-value = $0.04325$, so I reject null with 95% certainty, since $\alpha$ = $.05$
With this, I claim to say that there has been an increase of negative words since the mean is different, and seems to be larger two weeks after the election.
How do I calculate the actual percental increase? 


*

*Would I take the diffrence between $A_i$ and $B_i$ divided by $B_i$ and then cumulative their sum?


Any help would be wonderful!
 A: I would set it up as a regression with number of negative words as the dependent variable, post length and election-indicator as independent variables. To get a "percent change" interpretation, and to avoid nonsensical results (e.g. A model allowing for zero length and positive negative word count), take the natural log of the negative word count and the length.
The model would be:
ln("negative word count") = b0 + b1 * ln(length) + b2 * "Election-Indicator"
Where the third term equals 1 if the observation was taken during electionseason season, 0 otherwise.
Your null hypothesis is that b2=0, alt hypothesis is b2>0. Check the significance (both statistical and practical) of b2.
Interpreting this is easy. Because we took the log of the dependent variable, we can say that a 1 unit change in the election-indicator variable (I.e. If it is election season) roughly corresponds to a b2% increase in negative words, adjusted for length. Because we took the log of length, we can interpret b1 as a 1 % increase in length corresponds with a b1% increase in negative words.
