The Weka Forecasting plugin is a transformation step for PDI 4.x that is similar to the Weka Scoring Plugin. It can load or import a time series forecasting model created in Weka's time series analysis and forecasting environment and use it to generate a forecast for future time steps beyond the end of incoming historical data. This differs from the standard classification or regression scenario covered by the Weka Scoring plugin, where each incoming row receives a prediction (score) from the model, in that incoming rows provide a "window" over the recent history of the time series that the forecasting model then uses to initiate a closed-loop forecasting process to generate predictions for future time steps.
The Weka Forecasting plugin requires PDI 4 or higher, Weka 3.7.3 or higher and the core time series forecasting library from the time series forecasting package. Both Weka and the time series forecasting core library are bundled with the plugin, so no further downloads are required.
Before starting Kettle's Spoon UI, the Weka Forecasting plugin must be installed in either the plugins/steps directory in your Kettle distribution or in $HOME/.kettle/plugins/steps. Simply unpack the distribution zip file into plugins/steps and then start Spoon.
Once the forecasting plugin step is installed, and Spoon has been restarted, the Weka Forecasting step can be found in the "Transform" folder in the "Design" tab.
In this section we will demonstrate using the model developed on the Australian wine data in Section 3.1.1 of Time Series Analysis and Forecasting with Weka. This forecaster modeled monthly sales of the "Fortified" and "Dry-white" series. The following simple transformation loads the wine data and passes it to the Weka Forecasting step. Subsequent sections explain the configuration options for the step and the output that it produces.
Conceptually the UI for the Weka Forecasting step is set out in a similar fashion as the Weka Scoring plugin. The Model file tab allows a model to be loaded from the file system and configured for forecasting.