modeltime 1.0.0, we introduced Nested Forecasting as a way to forecast many time series iteratively. In
modeltime.ensemble 1.0.0, we introduce nested ensembles that can improve forecasting performance and be applied to many time series iteratively. We have added:
parsnip0.1.6 to align with xgboost upgrades.
recursive()function is extended to recursive ensembles for both single time series and multiple time series models (panel data).
NAwhen missing values are present in the sub-model predictions.
ensemble_model_spec()to support Panel Data (i.e. when data sets with multiple time series groups that have possibly overlapping time stamps).
modeltime.ensemblenow depends on
modeltime_fit_resamples()moved to a new package
ensemble_weighted(): Now removes models that have no weight (e.g. loading = 0). This speeds up refitting.
Stacked Ensembles (Breaking Changes)
The process for creating stacked ensembles is split into 2 steps:
modeltime_fit_resamples()to generate resampled predictions
ensemble_model_spec()to apply stacking using a
modeltime_refit(stacked_ensemble) is still one step, which is the best way to handle refitting since multiple stacked models may have different submodel compositions. An additional argument,
resamples can be provided to train stacked ensembles made with