Trains an AutoRegressive Integrated Moving Average (ARIMA) model. ARIMA model captures 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 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) If you encoutner errors please verify that Preferances > KNIME > Python (labs) > Python environment configuration is set to bundled