Analyze ARIMA Residuals

This component analyzes the residuals of an ARIMA (AutoRegressive Integrated Moving Average) model by
1. visualizing auto correlation of the residuals
2. performing Ljung-Box test of autocorrelation at lags 1-10
3. visualizing residuals in a line plot
4. calculating the four first central moments of the residuals
5. performing Jarque-Bera test of normality

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

Required extensions:
KNIME Data Generation
(https://hub.knime.com/knime/extensions/org.knime.features.datageneration/latest)
KNIME Expressions
(https://hub.knime.com/knime/extensions/org.knime.features.expressions/latest)
KNIME JavaScript Views
(https://hub.knime.com/knime/extensions/org.knime.features.js.views/latest)
KNIME Math Expression (JEP)
(https://hub.knime.com/knime/extensions/org.knime.features.ext.jep/latest)
KNIME Python Integration
(https://hub.knime.com/knime/extensions/org.knime.features.python2/latest)
KNIME Quick Forms
(https://hub.knime.com/knime/extensions/org.knime.features.js.quickforms/latest)

Input ports

1. Type: Table
   ARIMA Model
   ARIMA model