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.

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

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

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.

Nodes

• Math Formula30 ×
• Column Filter18 ×
• Integer Configuration18 ×
• Column Rename13 ×
• Column Rename (Regex)12 ×
• Show all 60 nodes

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Data Generation
• FeatureKNIME Expressions
• FeatureKNIME JavaScript Views
• FeatureKNIME JavaScript Views (Labs)
• Show all 11 modules

Links

• No links available