- Type: TableInput SeriesInput table containing a column to be forecasted.
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. 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
- Type: TableForecastFuture forecast values of target series and upper and lower bounds of 95% confidence interval.
- Type: TableInsample PredictionInsample model prediction on input rows.
- Type: TableError MetricsInsample error metrics of output model.
Used extensions & nodes
Created with KNIME Analytics Platform version 4.5.1
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