sktime: [BUG] UnobservedComponents throws error during predict() call, when wrapped under TransformedTargetForecaster and ForecastingPipeline

Describe the bug

Initially identified issue:

New version of sktime throws error for UnobservedComponents when wrapped under ForecastingGridSearchCV with TransformedTargetForecaster pipe.

Updated description:

From sktime v.0.11.1 onwards untill the current version of main, UnobservedComponents throws error during Predict method call when it is piped under TransformedTargetForecaster(). The same construct works with all the other models which I could test for example ARIMA, AutoETS etc. So the issue is how TransformedTargetForecaster calls predict and the cascading of the same method to UnobservedComponents class.

To Reproduce The code example was taken from documentation of ForecastingGridSearchCV for advanced example. The only thing changed was calling UnobservedComponents instead of ExponentialSmoothing at the end of param_grid argument in gscv.

from sktime.datasets import load_shampoo_sales
from sktime.forecasting.exp_smoothing import ExponentialSmoothing
from sktime.forecasting.naive import NaiveForecaster
from sktime.forecasting.model_selection import ExpandingWindowSplitter
from sktime.forecasting.model_selection import ForecastingGridSearchCV
from sktime.forecasting.compose import TransformedTargetForecaster
from sktime.forecasting.theta import ThetaForecaster
from sktime.transformations.series.impute import Imputer
from sktime.forecasting.structural import UnobservedComponents

y = load_shampoo_sales()
fh = [1,2,3]
pipe = TransformedTargetForecaster(steps=[
    ("imputer", Imputer()),
    ("forecaster", UnobservedComponents())])
cv = ExpandingWindowSplitter(
    initial_window=24,
    step_length=12,
    start_with_window=True,
    fh=[1,2,3])
gscv = ForecastingGridSearchCV(
    forecaster=pipe,
    param_grid=[{
        "forecaster": [NaiveForecaster(sp=12)],
        "forecaster__strategy": ["drift", "last", "mean"],
    },
    {
        "imputer__method": ["mean", "drift"],
        "forecaster": [ThetaForecaster(sp=12)],
    },
    {
        "imputer__method": ["mean", "last"],
        "forecaster": [UnobservedComponents()],
        "forecaster__seasonal": [12],
    },
    ],
    cv=cv,
    n_jobs=-1)
gscv.fit(y)

y_pred = gscv.predict(fh)

Expected behavior

Additional context Error report:

TypeError                                 Traceback (most recent call last)
~/work/chronos/chronos/pipeline/tests/test_pipeline.py in <module>
     37     cv=cv,
     38     n_jobs=-1)
---> 39 gscv.fit(y)
     40 
     41 y_pred = gscv.predict(fh)

~/work/sktime/sktime/forecasting/base/_base.py in fit(self, y, X, fh)
    262         # we call the ordinary _fit if no looping/vectorization needed
    263         if not vectorization_needed:
--> 264             self._fit(y=y_inner, X=X_inner, fh=fh)
    265         else:
    266             # otherwise we call the vectorized version of fit

~/work/sktime/sktime/forecasting/model_selection/_tune.py in _fit(self, y, X, fh)
    279 
    280         # Run grid-search cross-validation.
--> 281         results = self._run_search(evaluate_candidates)
    282 
    283         results = pd.DataFrame(results)

~/work/sktime/sktime/forecasting/model_selection/_tune.py in _run_search(self, evaluate_candidates)
    487         """Search all candidates in param_grid."""
    488         _check_param_grid(self.param_grid)
--> 489         return evaluate_candidates(ParameterGrid(self.param_grid))
    490 
    491     @classmethod

~/work/sktime/sktime/forecasting/model_selection/_tune.py in evaluate_candidates(candidate_params)
    266 
    267             out = parallel(
--> 268                 delayed(_fit_and_score)(params) for params in candidate_params
    269             )
    270 

~/opt/miniconda3/envs/chronos_dev2/lib/python3.7/site-packages/joblib/parallel.py in __call__(self, iterable)
   1054 
   1055             with self._backend.retrieval_context():
-> 1056                 self.retrieve()
   1057             # Make sure that we get a last message telling us we are done
   1058             elapsed_time = time.time() - self._start_time

~/opt/miniconda3/envs/chronos_dev2/lib/python3.7/site-packages/joblib/parallel.py in retrieve(self)
    933             try:
    934                 if getattr(self._backend, 'supports_timeout', False):
--> 935                     self._output.extend(job.get(timeout=self.timeout))
    936                 else:
    937                     self._output.extend(job.get())

~/opt/miniconda3/envs/chronos_dev2/lib/python3.7/site-packages/joblib/_parallel_backends.py in wrap_future_result(future, timeout)
    540         AsyncResults.get from multiprocessing."""
    541         try:
--> 542             return future.result(timeout=timeout)
    543         except CfTimeoutError as e:
    544             raise TimeoutError from e

~/opt/miniconda3/envs/chronos_dev2/lib/python3.7/concurrent/futures/_base.py in result(self, timeout)
    433                 raise CancelledError()
    434             elif self._state == FINISHED:
--> 435                 return self.__get_result()
    436             else:
    437                 raise TimeoutError()

~/opt/miniconda3/envs/chronos_dev2/lib/python3.7/concurrent/futures/_base.py in __get_result(self)
    382     def __get_result(self):
    383         if self._exception:
--> 384             raise self._exception
    385         else:
    386             return self._result

TypeError: No valid mtype could be identified

Versions System: python: 3.7.13 (default, Mar 28 2022, 07:24:34) [Clang 12.0.0 ] executable: …/miniconda3/envs/chronos_dev2/bin/python machine: Darwin-21.4.0-x86_64-i386-64bit

Python dependencies: pip: 21.2.2 setuptools: 58.0.4 sklearn: 1.0.2 sktime: 0.11.3 statsmodels: 0.12.1 numpy: 1.21.5 scipy: 1.7.3 pandas: 1.3.5 matplotlib: 3.5.1 joblib: 1.1.0 numba: 0.55.1 pmdarima: 1.8.5 tsfresh: None