Statistical test - Time-series - Marketing I want to compare to see if there is a statistically significant difference
between the mean of times-series A and time-series B.
Each time series represent the amount of daily revenue for each campaign. Both campaigns are targeting a different list of users. Each user can be in one of the lists but not in another.
Both time series have lag 1 autocorrelation of  > 0.5, so it seems t-test should not be used here.
Is there a statistical test I can use to compare the means of the groups?

When two campaigns' values follow the same trend through time,  their difference through time should be just noise with no trend; this defined as an ARIMA (0,0,0,0) model.
When we apply ARIMA modeling to Campaign A -Campaign B  we have the best model (using R function auto.arima) is ARIMA(0,0,0,0) with mean 21.0. Hence, we have noise with no trend or autocorrelation close to zero.
Then, we can use the standard T test for the difference  Campaign A -Campaign B; for your dataset we get t = 2.4592, df = 19, p-value = 0.02369 and mean difference is 21.05
When the difference is not white noise, then, mostlikely will change through time.
