I am trying to forecast hourly wind speeds for a couple of years. My dataset contained trends and seasonality so a applied a difference of 24 to remove seasonality and then another difference to remove trends. This is the resulting autocorrelation and partial autocorrelation function. I would like to know how to determine the order of the SARIMA model= (p,d,q)*(P,D,Q)[s]?
My best suggestion would be to feed your series into R as a
ts object and then let
auto.arima() from the
forecast package select an appropriate seasonality. This is an established piece of software that will almost certainly do a better job than if you applied something you read about here. (If you do want to "roll your own", you can read up on the OCSB test for seasonality that
However, and more importantly, you may have multiple-seasonalities, since wind speeds may not only show day-over-day patterns, but also year-over-year ones, so you have cycles of length both $24$ and $365.25\times 24=8766$. Multiple seasonalities can be modeled and forecasted using the
tbats() function in the
forecast package. See also the multiple-seasonalities tag info.