Panel Data
ensemble_average()
, ensemble_weighted()
and ensemble_model_spec()
to support Panel Data (i.e. when data sets with multiple time series groups that have possibly overlapping time stamps).Changes
modeltime.ensemble
now depends on modeltime.resample
for the modeltime_fit_resamples()
functionality.modeltime_fit_resamples()
moved to a new package modeltime.resample
.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 predictionsensemble_model_spec()
to apply stacking using a model_spec
Note - 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 ensemble_model_spec()
.
modeltime.ensemble
.