ARIMA Predictor

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)

Input ports

1. Type: Python
   ARIMA Model
   ARIMA Model.

Output ports

1. Type: Table
   Forecast
   Forecasted values and their standard errors.
2. Type: Table
   In-Sample Predictions
   Model predictions on data points in the training data. Caclulated according to Level and Type configurations.