But besides the conventional formulation, there are also state space forms of ARIMA, the most relevant to our topic being the one implemented in SSOE form (Chapter 11 of Hyndman et al., 2008). Svetunkov and Boylan (2020) adapted this state space model for supply chain forecasting, developing an order selection mechanism, sidestepping the hypothesis testing and focusing on information criteria. However, the main issue with that approach is that the resulting ARIMA model works very slow on the data with high frequencies (because the model was formulated based on Chapter 11 of Hyndman et al. (2008)). Luckily, an alternative SSOE state space formulation is introduced in Chapter 5.1. This model is already implemented in the msarima() function of the smooth package and was also used as the basis for the ADAM ARIMA.