I am wondering what is the best way to extract knowledge about periodic time series data?
In my case, I am trying to analyse historical hourly electricity prices to get information about its volatility, when the peaks occur, what is its distribution among the hours of the day or something similar to be able to make good decisions on when to throttle devices to save money and stay energy efficient.
My original idea was to simply aggregate the time series by calculating mean values for each hour over a period of several months and then sort them to pick the top M% hours and say that they are the most expensive on the average and should be avoided.
This is a very crude approach, so I'm wondering whether I'm making some obvious mistake, disregarding some information? Is there some better approach? It seems to me that the information on how high the current prices are in comparison to the average should also be used to sometimes exclude, say, 3 hours from a day and on other times only 1 hour.