model = ARIMA(df.value, order=(1,1,1))
fitted = model.fit(disp=0)
fc, se, conf = fitted.forecast(...)
Probably it is so because in continuous case there is the luxury of having enough points between any other points. In discrete case you are much more constrained, for example, you cannot choose a point between 0 and 1, and what is the length of the diagonal of a binary cube or angle between its two hyper-planes? Sometimes these notions can be naturally defined but in other cases the formal theory is not so natural.
By the way, an interesting question is how complex boolean numbers could be defined (naturally).