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.

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

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.

Nodes

Extensions

Links

Download

To use this component in KNIME, download it from the below URL and open it in KNIME:

Download Component
On NodePit since: 2022-06-07
Last update: 2026-01-22
Created with KNIME version: v4.4.0

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