SARIMA Predictor

Computes predictions from an estimated Seasonal AutoRegressive Integrated Moving Average (SARIMA) model.

Two types of predictions are computed:
1. Forecast: forecast of the given time series h periods ahead.
2. In-Sample Prediction: generates prediction in the range of the training data.
%%00009* If Dynamic is enabled lagged predictions are used, otherwise lagged true values are used.
%%00009* Level setting determines whether in-sample differenced or original values are output. If no differencing in ARIMA model, this setting has no effect.

If you encoutner errors please verify that
Preferances > KNIME > Python (labs) > Python environment configuration
is set to bundled

Options

Dynamic
Check this box to use in-sample predictions as lagged values. Otherwise use true values.
Number of periods to forecast
How many periods ahead should be forecasted.
Type
*Only affects models with a non-zero value for the "I" component.%%00010linear: linear prediction of the differenced time series.%%00010levels: predict the values of the original time series.

Input Ports

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SARIMA Model.

Output Ports

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Forecasted values and their standard errors.
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In sample prediction from the SARIMA model, only populated if training with no seasonal terms.

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