Trains a Seasonal AutoRegressive Integrated Moving Average (SARIMA) model. SARIMA models capture temporal structures in time series data in the following components: - AR: Relationship between the current observation and a number (p) of lagged observations - I: Degree (d) of differencing required to make the time series stationary - MA: Time series mean and the relationship between the current forecast error and a number (q) of lagged forecast errors *Seasonal versions of these operate similarly with lag intervals equal to the seasonal period (S). Additionally, coefficent statistics and residuals are provided as table outputs. Model Summary metrics: RMSE (Root Mean Square Error) MAE (Mean Absolute Error) MAPE (Mean Absolute Percentage Error) *will be missing if zeroes in target R2 (Coefficient of Determination) Log Likelihood AIC (Akaike Information Criterion) BIC (Bayesian Information Criterion) 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