Often, some pipelines require the ensembling of different statistical models. We can accomplish this by introducing a new module in the library named statsforecast/ensembles.py, which contains various methods for ensembling models. The general implementation might look something like this:
from statsforecast.ensembles import FFORMA, MedianEnsemble
sf = StatsForecast(
models=[FFORMA(AutoARIMA(), AutoETS()), MedianEnsemble(Naive(), SeasonalNaive())]
)
Ideally, the output dataframe should include the forecasts from individual models as well as the ensemble method's forecast.
Description
Often, some pipelines require the ensembling of different statistical models. We can accomplish this by introducing a new module in the library named
statsforecast/ensembles.py
, which contains various methods for ensembling models. The general implementation might look something like this:Ideally, the output dataframe should include the forecasts from individual models as well as the ensemble method's forecast.
Use case
No response