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. If you encoutner errors please verify that Preferances > KNIME > Python (labs) > Python environment configuration is set to bundled