[BUG] statsforecast 1.6.0 compatibility - fix argument differences …

…between `sktime` and `statsforecast` (#5393)

#### Reference Issues/PRs
Fixes #5295
Depends on #5317, merge after #5317


#### What does this implement/fix? Explain your changes.
Adds parameters `stl_kwargs` and `pred_int_kwargs`, keeping up with
`statsforecast` new release

#### Did you add any tests for the change?
Added test params to `StatsForecastMSTL`, all tests in `check_estimator`
pass

sktime/forecasting/base/adapters/_generalised_statsforecast.py

@@ -447,10 +447,17 @@ def __init__(self, estimator):
         super().__init__()
 
         self.estimator = estimator
+        self.prediction_intervals = None
 
     def __repr__(self):
         return "StatsForecastBackAdapter"
 
+    def new(self):
+        """Make new instance of back-adapter."""
+        _self = type(self).__new__(type(self))
+        _self.__dict__.update(self.__dict__)
+        return _self
+
     def fit(self, y, X=None):
         """Fit to training data.
 
@@ -489,6 +496,12 @@ def predict(self, h, X=None, level=None):
         mean = self.estimator.predict(fh=range(1, h + 1), X=X)[:, 0]
         if level is None:
             return {"mean": mean}
+        # if a level is passed, and if prediction_intervals has not been instantiated
+        # yet
+        elif self.prediction_intervals is None:
+            from statsforecast.utils import ConformalIntervals
+
+            self.prediction_intervals = ConformalIntervals(h=h)
 
         level = sorted(level)
         coverage = [round(1 - (_l / 100), 2) for _l in level]

sktime/forecasting/statsforecast.py

@@ -595,6 +595,12 @@ class StatsForecastMSTL(_GeneralisedStatsForecastAdapter):
     trend_forecaster : estimator, optional, default=StatsForecastAutoETS()
         Sktime estimator used to make univariate forecasts. Multivariate estimators are
         not supported.
+    stl_kwargs : dict, optional
+        Extra arguments to pass to [`statsmodels.tsa.seasonal.STL`]
+        (https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html#statsmodels.tsa.seasonal.STL).
+        The `period` and `seasonal` arguments are reserved.
+    pred_int_kwargs : dict, optional
+        Extra arguments to pass to [`statsforecast.utils.ConformalIntervals`].
 
     References
     ----------
@@ -623,6 +629,8 @@ def __init__(
         self,
         season_length: Union[int, List[int]],
         trend_forecaster=None,
+        stl_kwargs: Optional[Dict] = None,
+        pred_int_kwargs: Optional[Dict] = None,
     ):
         super().__init__()
 
@@ -634,6 +642,8 @@ def __init__(
             self._trend_forecaster = clone(trend_forecaster)
         else:
             self._trend_forecaster = StatsForecastAutoETS(model="ZZN")
+        self.stl_kwargs = stl_kwargs
+        self.pred_int_kwargs = pred_int_kwargs
 
         # checks if trend_forecaster is already wrapped with
         # StatsForecastBackAdapter
@@ -650,6 +660,14 @@ def __init__(
                     "forecaster."
                 )
 
+        # check if prediction interval kwargs are passed
+        if self.pred_int_kwargs:
+            from statsforecast.utils import ConformalIntervals
+
+            self._trend_forecaster.prediction_intervals = ConformalIntervals(
+                **self.pred_int_kwargs
+            )
+
     def _get_statsforecast_class(self):
         from statsforecast.models import MSTL
 
@@ -695,6 +713,12 @@ def get_test_params(cls, parameter_set="default"):
                 {
                     "season_length": 4,
                 },
+                {
+                    "season_length": 4,
+                    "pred_int_kwargs": {
+                        "n_windows": 2,
+                    },
+                },
             ]
         except ModuleNotFoundError:
             from sktime.forecasting.naive import NaiveForecaster