What I usually do to forecast is:

  1. Train a model (i.e. bats, stl + arima|ets, ANN, ELM, etc ...) through a model function obtaining a trained model object, based on information criteria or cross-validation for time series.
  2. Use a forecast|predict method to obtain iterative h step-ahead forecasts using the trained model.
  3. Evaluate forecast accuracy (for each horizon up to h)

All the above considering rolling forecasting origin techniques that allow to move the training and test sets in time and specify a model for each window.

I have noticed that several methods found in R packages have a model parameter that (if specified) apply an existing model to a new data set. Considering the rolling forecasting scheme described above:

  • When it is appropriate to use the model argument?
  • Is this option an alternative to fitting a model for each horizon as in the rolling window scheme?
  • What does the ''If model is passed, this same model is fitted to y without re-estimating any parameters'' description implies to forecasting procedures?

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