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)