Computes predictions from an estimated AutoRegressive Integrated Moving Average (ARIMA) 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: https://www.knime.com/blog/setting-up-the-knime-python-extension-revisited-for-python-30-and-20 Python script is used due to performance reasons. KNIME Autoregressive integrated moving average (ARIMA) extension provides an alternative ARIMA Predictor node: https://kni.me/e/5_ZZ3nif8tLRjGji Required extensions: KNIME Python Integration (https://hub.knime.com/knime/extensions/org.knime.features.python2/latest) KNIME Quick Forms (https://hub.knime.com/knime/extensions/org.knime.features.js.quickforms/latest)
- Type: Python ARIMA Model.
- Type: Data Forecasted values and their standard errors.
- Type: Data Model predictions on data points in the training data. Caclulated according to Level and Type configurations.