# How to statistically measure the before and after effects of a change in a time series

I have a time series which represents call attempts to a cellular network. The data was recorded over 7 days using hourly intervals. I plan to make a brief change (for only a few hours) to the network which will either increase or decrease the call attempt value during that period of change.

How can I measure with confidence whether my brief change had any impact using statistics? I've read about change point analysis here but knowing my data, the changes in my data could be completely random due to the time of the day or day of the week etc and it could highlight multiple change points that mean nothing.

I'm afraid that my change will be masked by temporal events in the data and I will not be certain if my change had any impact.

Below is a sample of what a single cell variable looks like over 7 days. If you have any suggestions please shout.

• check out the CausalImpact R package: google.github.io/CausalImpact/CausalImpact.html, with an introduction video here: youtube.com/watch?v=GTgZfCltMm8. I don't like all the fake or perfect data circumstances in their examples, so here's something I did with it: markhw.com/blog/viceroy-mac-demarco – Mark White Apr 16 '18 at 23:12
• @MarkWhite, thanks for the reply. Rather than just throw my data into a black box I am reading the original paper to get a better intuition about the workings of the model. My data above is from a cell site in say Florida where my intervention was solely focused, I have similar time series from all over the country, can I use the same variable (call attempts) from California, Boston, Chicago...... as the predictor variables? – TheGoat Apr 17 '18 at 23:05
• yeah, that seems reasonable to me. You’re basically using all the covariates to build a model that will estimate what things would have been like IF you didn’t do the treatment. Then it just compares what you DID observe to this estimated counterfactual. I would look up the potential outcomes framework—sometimes called the Rubin causal model in more technical papers—for a theoretical background on that idea of counterfactuals. – Mark White Apr 18 '18 at 4:36