7/8/2023 0 Comments Install weka packagfes![]() ![]() This is called "closed loop" forecasting. ![]() ![]() Another test instance is then created from the history window and then the next time step is forecasted, etc. Once the forecaster produces a prediction for the next time step, this forecasted value moves into the sliding window as the most recent value of the target and the oldest value in the window falls out. corresponds to the longest lag used by the forecaster). So, the priming data typically just needs to be enough historical instances to fill the window (i.e. Priming is simply inputing enough historical data to "populate" this sliding window and hence create a single test instance that can kick off the closed-loop forecasting process for future time steps. This process effectively removes the time dependency in the original target since this is captured by the shifted attributes (essentially a sliding window). instances containing these shifted values and the current target value are presented as standard propositional instances to the underlying learning algorithm. What this means is that in order to model the time dependency it creates copies of the target field that are shifted in time. Weka's time series forecasting is built on standard propositional machine learning algorithms.
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