I'm working through this tutorial and this guy run SARIMAX model for a time series with both seasonal and trend components:
# create SARIMAX model with previously determined lags # sar_m = sarimax.SARIMAX(ts15_train.values, trend='n', order=(2,1,1), seasonal_order=(2, 1, 1, 24), simple_differencing=False).fit()
It seems wrong to me. Am I right here? I read some tutorials here and there and I believe we should eliminate both seasonal and trend components first to make a time series stationary (by performing some transforming operations like ts = log(ts) etc), then predict (e.g. by ARIMA model the next K values) and then bring back our seasonal and trend components (e.g. add a running mean, pow(2, x)).