I'm trying to understand how best to approach the hypothetical example below.
If have a data series which represents the load time of an web application and the load time increases as more people use the app.
I have hourly average data going back x number of months. The dataset shows a weekly trend (Fridays appear similar to other Fridays) and an hourly trend (8pm looks similar to 8pm the day prior).
Assume that the hourly data has a bimodal or normal distribution. I'm keen to understand how this impacts the thought process.
My end goal it to remove the seasonality added by the peaks and troughs that the additional users create to see the load times independent of the users. I.e. when in load time small or large for another factor.
When i've been thinking about this and doing some digging (i have limited statistical knowledge at this point) i have considered differencing on an hourly basis (comparing Wednesday 8pm to the prior Wednesday at 8pm) due to a few online examples with weather data that seemed in some respect to map.
I'd really like to understand how someone would approach this problem and the thought processes they would go through to get the desired outcome.