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But besides the conventional formulation, there is also a state space form of ARIMA, implemented in SSOE . adapted this state space model for supply chain forecasting, developing an order selection mechanism, sidesteping the hypothesis testing and focusing on information criteria. However, the main issue with this approach is that the resulting ARIMA model works very slow on the data with large frequencies (because the transition matrix becomes huge). Luckily, there is an alternative SSOE state space formulation, using the same idea of lags as ADAM ETS. This model is already implemented in msarima() function of smooth package and was also used as the basis for the ADAM ARIMA.