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.
* If Dynamic is enabled lagged predictions are used, otherwise lagged true values are used.
* Level setting determines whether in-sample differenced or original values are output. If no differencing in ARIMA model, this setting has no effect.

Note: This component requires a Python environment with StatsModels package installed. In this blog post we explain how to setup the KNIME Python extension:

knime.com/blog/setting-up-the-knime-python-extension-revisited-for-python-30-and-20

Input ports

1. Type: Python
   SARIMA Model
   SARIMA Model.

Output ports

1. Type: Table
   Forecast
   Forecasted values and their standard errors.
2. Type: Table
   In-Sample
   In sample prediction from the SARIMA model, only populated if training with no seasonal terms.