Auto-SARIMA

This component uses a heuristic approach to analyze the target series and fit a (S)ARIMA model for forecasting with automatically configured hyper-parameters.

An interactive view is produced to help interpret and visualize the model used and forecasts generated.

Before using this component verify that there are no missing values in your target series. If there are, you can impute them externally with the missing value node.

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

Options

Target Column
Select a column to generate a forecast for.
Forecast Length
Select the number of forecasts to generate.
Backwards Length
Select the number of data points from the end of the target column to display in the interactive view.

Input Ports

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Input table containing a column to be forecasted.

Output Ports

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Future forecast values of target series.
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Insample model prediction on input rows.
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Insample error metrics of output model.

Nodes

Extensions

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