[Python] sktime got error when performing ForecastingGridSearchCV

I'm trying to perform scaling on both y and X before fitting the forecast model. I want to make the scaling transformation optional. However, I encountered an error when performing the grid search above.

from sklearn.preprocessing import MinMaxScaler, PowerTransformer, RobustScaler
from sktime.performance_metrics.forecasting import MeanSquaredError
from sktime.forecasting.statsforecast import StatsForecastAutoARIMA
from sktime.forecasting.model_selection import ExpandingWindowSplitter
from sktime.forecasting.compose import ForecastingPipeline
from sktime.forecasting.fbprophet import Prophet


train1

y M0 Delta
ds
2022-10 127 249.0 14.0
2022-11 194 298.0 160.0
2022-12 202 269.0 128.0
2023-01 149 215.0 33.0
2023-02 203 198.0 111.0
2023-03 222 259.0 10.0
2023-04 195 232.0 -1.0
2023-05 236 210.0 47.0
2023-06 76 155.0 -12.0
2023-07 204 232.0 101.0
2023-08 231 221.0 -13.0
2023-09 171 196.0 -30.0
2023-10 172 80.0 39.0
2023-11 173 206.0 1.0
2023-12 132 189.0 -8.0
2024-01 165 190.0 50.0
2024-02 93 136.0 0.0
2024-03 105 126.0 45.0
2024-04 99 134.0 18.0
2024-05 121 128.0 12.0
2024-06 109 181.0 0.0
2024-07 183 244.0 7.0
2024-08 159 195.0 27.0
2024-09 147 186.0 -5.0


test1

M0 Delta
ds
2024-10 161.0 -10.0


Forecasting Pipeline

pipe_y = TransformedTargetForecaster(
steps=[
("scaler", OptionalPassthrough(TabularToSeriesAdaptor(RobustScaler()))),
("forecaster", StatsForecastAutoARIMA(sp=12)),
]
)
pipe_X = ForecastingPipeline(
steps=[
("scaler", OptionalPassthrough(TabularToSeriesAdaptor(RobustScaler()))),
("forecaster", pipe_y),
]
)

cv1 = ExpandingWindowSplitter(fh=[1], initial_window=train1.shape[0]-3, step_length=1)

gscv1 = ForecastingGridSearchCV(
forecaster=pipe_X,
param_grid=[
{
"scaler__passthrough": [True, False],
"forecaster__scaler__passthrough": [True, False],
"forecaster": [StatsForecastAutoARIMA(sp=12)],
},
{
"scaler__passthrough": [True, False],
"forecaster": [Prophet()],
"forecaster__scaler__passthrough": [True, False],
"forecaster__seasonality_mode": ['addictive','multiplicative'],
"forecaster__changepoint_prior_scale": [0.001, 0.01, 0.1, 0.5],
"forecaster__seasonality_prior_scale": [0.01, 0.1, 1.0, 10.0],
},
],
cv=cv1,
error_score="raise",
scoring=MeanSquaredError(square_root=True),
)

gscv1.fit(train1['y'], X=train1[features1])


Error Traceback

---------------------------------------------------------------------------
_RemoteTraceback Traceback (most recent call last)
_RemoteTraceback:
"""
Traceback (most recent call last):
File "c:\Desktop\Waterfall Chart\lib\site-packages\joblib\externals\loky\process_executor.py", line 463, in _process_worker
r = call_item()
File "c:\Desktop\Waterfall Chart\lib\site-packages\joblib\externals\loky\process_executor.py", line 291, in __call__
return self.fn(*self.args, **self.kwargs)
File "c:\Desktop\Waterfall Chart\lib\site-packages\joblib\parallel.py", line 598, in __call__
return [func(*args, **kwargs)
File "c:\Desktop\Waterfall Chart\lib\site-packages\joblib\parallel.py", line 598, in <listcomp>
return [func(*args, **kwargs)
File "c:\Desktop\Waterfall Chart\lib\site-packages\sktime\forecasting\model_selection\_tune.py", line 368, in _fit_and_score
forecaster.set_params(**params)
File "c:\Desktop\Waterfall Chart\lib\site-packages\sktime\base\_meta.py", line 60, in set_params
self._set_params(steps_attr, **kwargs)
File "c:\Desktop\Waterfall Chart\lib\site-packages\sktime\base\_meta.py", line 142, in _set_params
super().set_params(**params)
File "c:\Desktop\Waterfall Chart\lib\site-packages\skbase\base\_base.py", line 398, in set_params
unmatched_params = {key: params[key] for key in unmatched_keys}
File "c:\Desktop\Waterfall Chart\lib\site-packages\skbase\base\_base.py", line 398, in <dictcomp>
unmatched_params = {key: params[key] for key in unmatched_keys}
KeyError: 'estimator'
"""
...
--> 763 raise self._result
764 return self._result
765 finally:

KeyError: 'estimator'


I'm trying to follow the automl example in the link below: https://www.sktime.net/en/latest/examples/03b_forecasting_transformers_pipelines_tuning.html

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