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Publication date: 11/10/2021

State Space Smoothing Models

Shows the Specify State Space Smoothing Models window, which enables you to fit a variety of state space smoothing models as defined by Hyndman et al. (2008). A state space smoothing model is defined based on its error, trend component, and seasonal component.

The errors can be additive (A) or multiplicative (M).

The trend component can be none (N), additive (A), additive damped (Ad), multiplicative (M), or multiplicative damped (Md).

The seasonal component can be none (N), additive (A), or multiplicative (M).

A specific model can be represented by its ETS (Error, Trend, Seasonal). Use the check boxes in the Specify State Space Smoothing Models window to select the error, trend, and seasonality for the desired models. Click Select Recommended to select the check boxes that correspond to the models recommended by the platform. The window opens with the recommended models selected. Click Select All to select all check boxes or Deselect All to deselect all check boxes. The window also contains the following options:


Specifies seasonality values to be considered in the model fitting process.

Constrain Parameters

Constrains the parameters in such a way that the further an observation is from the present, the less effect it has on the present state of the model. In State Space Smoothing models, a forecast at time t, given all previous observations, is the same as the weighted sum of all observations up to time t. The weights are a function of the parameters. Therefore, constraining the parameters ensures that the weights for past observations go to zero and that the further an observation is from the present, the faster the weight goes to zero.

When you click OK, the specified set of models is fit. Summary values for each state space smoothing model are added to the Model Comparison table. Individual fit reports are added to the report window.

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