The data consists of a time series with daily frequency. It is basically temperature data, and I can see a clear seasonal pattern throughout the year.
I would like to model this time series using statsmodels SARIMAX method. Since I have daily data and a yearly seasonal pattern, I chose s=365 in the seasonal order parameter. Now my understanding of the other seasonality parameters is as follows: In (P,D,Q,s) I first determined s as before. Then, since s is positive, I chose P=1 and Q=0. My time series is stationary, so I chose D=0. This leads to the following code:
order = (3,0,1)
seasonal_order = (1,1,0,365)
model = sm.tsa.statespace.SARIMAX(
Now this causes two problems: First of all, the method takes way too long to calculate, and I don't get any results. Second of all, if I would get results I suspect my model would be heavily overfitted, as to my understanding this adds 364 extra parameters to it. I am struggling with the method's documentation, hence why I am asking my question here. Is there any way to overcome this problem? I would be happy to include monthly seasonality, however simply setting s=12 doesn't work, and I don't want to aggregate my data to monthly data.