Tiny Dataset error while fitting about statsforecast HOT 14 CLOSED

try:
    models = [HoltWinters(season_length=12, error_type='A'),
              SeasonalNaive(season_length=12),
              HistoricAverage(),
              DOT(season_length=12, decomposition_type='additive'),
              AutoTheta(season_length=12),
              AutoARIMA(season_length=12),
              AutoETS(season_length=12)
             ]
    #instaniate the model
    model = StatsForecast(models = models, 
                          freq='M', 
                          n_jobs=-1
                          fallback_model= HoltWinters(season_length=12, error_type='A')
                         )

    #train model, like in sklearn
    model.fit(df=X_train_agg.head(1000))

#this circumvents the error we get with autoarima
#try again without autoarima in the list of models
except ZeroDivisionError:
    models = [HoltWinters(season_length=12, error_type='A'),
              SeasonalNaive(season_length=12),
              HistoricAverage(),
              DOT(season_length=12, decomposition_type='additive'),
              AutoTheta(season_length=12),
              AutoETS(season_length=12)
             ]

    model = StatsForecast(models = models, 
                      freq='M', 
                      n_jobs=-1,
                      #fallback_model= HoltWinters(season_length=12, error_type='A')
                     )

    model.fit(X_train_agg)

value_counts = new_df['unique_id'].value_counts()
valid_indices = value_counts[value_counts >= 5].index
filtered_df = new_df[new_df['unique_id'].isin(valid_indices)]
filtered_df['unique_id'].value_counts()

**OUTPUT:**
HK                   74
HK/Pharma/HP1_S01    74
HK/Pharma/HP1_J05    74
HK/Pharma/HP1_J02    74
HK/Pharma/HP1_J01    74
                     ..
HK/CHC/TD1_C07        6
HK/CHC/TD1_A10        6
HK/CHC/HP1_A10        5
HK/CHC/DP1_A10        5
HK/Pharma/HP1_P02     5
Name: unique_id, Length: 482, dtype: int64

**ERROR AFTER USING THIS df:**
Traceback (most recent call last):
  File "/opt/conda/lib/python3.10/multiprocessing/pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "/opt/conda/lib/python3.10/site-packages/statsforecast/core.py", line 73, in fit
    fm[i, i_model] = new_model.fit(y=y, X=X)
  File "/opt/conda/lib/python3.10/site-packages/statsforecast/models.py", line 553, in fit
    self.model_ = ets_f(
  File "/opt/conda/lib/python3.10/site-packages/statsforecast/ets.py", line 1235, in ets_f
    raise NotImplementedError("tiny datasets")
NotImplementedError: tiny datasets
"""

The above exception was the direct cause of the following exception:

NotImplementedError                       Traceback (most recent call last)
Cell In[50], line 57
     50     model = StatsForecast(models = models, 
     51                           freq='M', 
     52                           n_jobs=-1
     53                           #fallback_model= HoltWinters(season_length=12, error_type='A')
     54                          )
     56     #train model, like in sklearn
---> 57     model.fit(df=filtered_df.head(1000))
     59 #this circumvents the error we get with autoarima
     60 #try again without autoarima in the list of models
     61 except ZeroDivisionError:

File /opt/conda/lib/python3.10/site-packages/statsforecast/core.py:581, in _StatsForecast.fit(self, df, sort_df)
    579     self.fitted_ = self.ga.fit(models=self.models)
    580 else:
--> 581     self.fitted_ = self._fit_parallel()
    582 return self

File /opt/conda/lib/python3.10/site-packages/statsforecast/core.py:940, in _StatsForecast._fit_parallel(self)
    938         future = executor.apply_async(ga.fit, (self.models,))
    939         futures.append(future)
--> 940     fm = np.vstack([f.get() for f in futures])
    941 return fm

File /opt/conda/lib/python3.10/site-packages/statsforecast/core.py:940, in <listcomp>(.0)
    938         future = executor.apply_async(ga.fit, (self.models,))
    939         futures.append(future)
--> 940     fm = np.vstack([f.get() for f in futures])
    941 return fm

File /opt/conda/lib/python3.10/multiprocessing/pool.py:774, in ApplyResult.get(self, timeout)
    772     return self._value
    773 else:
--> 774     raise self._value

NotImplementedError: tiny datasets