8.10 ARIMA vs ETS

It is a commonly held myth that ARIMA models are more general than exponential smoothing. While linear exponential smoothing models are all special cases of ARIMA models, the non-linear exponential smoothing models have no equivalent ARIMA counterparts. On the other hand, there are also many ARIMA models that have no exponential smoothing counterparts. In particular, all ETS models are non-stationary, while some ARIMA models are stationary.

The ETS models with seasonality or non-damped trend or both have two unit roots (i.e., they need two levels of differencing to make them stationary). All other ETS models have one unit root (they need one level of differencing to make them stationary).

The following table gives the equivalence relationships for the two classes of models.

ETS model ARIMA model Parameters
ETS(A,N,N) ARIMA(0,1,1) \(\theta_1 = \alpha-1\)
ETS(A,A,N) ARIMA(0,2,2) \(\theta_1 = \alpha+\beta-2\)
\(\theta_2 = 1-\alpha\)
ETS(A,A,N) ARIMA(1,1,2) \(\phi_1=\phi\)
\(\theta_1 = \alpha+\phi\beta-1-\phi\)
\(\theta_2 = (1-\alpha)\phi\)
ETS(A,N,A) ARIMA(0,0,\(m\))(0,1,0)\(_m\)
ETS(A,A,A) ARIMA(0,1,\(m+1\))(0,1,0)\(_m\)
ETS(A,A,A) ARIMA(1,0,\(m+1\))(0,1,0)\(_m\)

For the seasonal models, the ARIMA parameters have a large number of restrictions.