I've been reading the whitepaper that accompanied Facebook's release of Prophet, it's time-series modeling library. One topic the authors drew attention to was that noise was assumed to be iid; they note, that this assumption goes against the grain for traditional time-series solutions, such as ARIMA. Likewise, their solution doesn't account for autocorrelation or moving averages whatsoever.
In general, the Prophet model accounts for piecewise linear (or logistic growth) trends, seasonality, and holiday effects (where seasonality is captured via a fourier series.)
I'm curious, why are autocorrelation, moving average, and non-iid noise emphasized in traditional time-series approaches, such as ARIMA? Wouldn't it be easier to just use a GLM where seasonal controls (whether that be month, week, etc) could be used to augment the overall linear (or logistic trend)?