- Type: PythonSARIMA ModelSARIMA Model.
Computes predictions from an estimated Seasonal AutoRegressive Integrated Moving Average (SARIMA) model. 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 Dynamic is enabled lagged predictions are used, otherwise lagged true values are used. * Level setting determines whether in-sample differenced or original values are output. If no differencing in ARIMA model, this setting has no effect. 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
- Type: TableForecastForecasted values and their standard errors.
- Type: TableIn-SampleIn sample prediction from the SARIMA model, only populated if training with no seasonal terms.
Used extensions & nodes
Created with KNIME Analytics Platform version 4.4.0
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