How to use best model and transformer from forecaster pipeline on new data without actual y

[fstayco]

Hi @mikekeith52 . Sorry to ask again. Am still getting familiar with scalecast. This is related to #57 .

I got the following from the forecaster pipeline I ran:

best model = knn best params = {'n_neighbors': 43} optimal transformer = Transformer( transformers = [ ('DetrendTransform', {'loess': True}), ('DiffTransform', 1), ('ScaleTransform',) ] ) f = Forecaster( DateStartActuals=2016-01-10T00:00:00.000000000 DateEndActuals=2021-01-10T00:00:00.000000000 Freq=None N_actuals=260 ForecastLength=4 Xvars=['month_8', 'quarter_2', 'quarter_3', 'COVID19', 'dengue_lag_4', 'dengue_lag_5', 'dengue_lag_6', 'dengue_lag_7', 'dengue_lag_8', 'symptoms_of_dengue_lag_8'] TestLength=4 ValidationMetric=rmse ForecastsEvaluated=['mlr', 'lasso', 'ridge', 'elasticnet', 'xgboost', 'lightgbm', 'knn'] CILevel=None CurrentEstimator=knn GridsFile=Grids )

Is there a way I can use these in sklearn (if not scalecast) to forecast new values of y based on just the values of Xvars? From what I understand forecaster needs y.

I would appreciate your assistance a lot.