INFO:nixtlats.timegpt:Validating inputs...
INFO:nixtlats.timegpt:Preprocessing dataframes...
WARNING:nixtlats.timegpt:The specified horizon "h" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.
INFO:nixtlats.timegpt:Using the following exogenous variables: trading
INFO:nixtlats.timegpt:Calling Forecast Endpoint...
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3628             try:
-> 3629                 return self._engine.get_loc(casted_key)
   3630             except KeyError as err:

~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

~\anaconda3\lib\site-packages\pandas\_libs\index.pyx in pandas._libs.index.IndexEngine.get_loc()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

pandas\_libs\hashtable_class_helper.pxi in pandas._libs.hashtable.PyObjectHashTable.get_item()

KeyError: 'TimeGPT-lo-20'

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

KeyError                                  Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_17372\1034047663.py in <module>
      1 list = [0.4, 0.6]
      2 
----> 3 fy25_pax_preds_df = timegpt.forecast(
      4     df=historical_pax_df,
      5     X_df=future_pax_exogenous_df,

~\anaconda3\lib\site-packages\nixtlats\timegpt.py in forecast(self, df, h, freq, id_col, time_col, target_col, X_df, level, quantiles, finetune_steps, finetune_loss, clean_ex_first, validate_token, add_history, date_features, date_features_to_one_hot, model, num_partitions)
   1135         """
   1136         if isinstance(df, pd.DataFrame):
-> 1137             return self._forecast(
   1138                 df=df,
   1139                 h=h,

~\anaconda3\lib\site-packages\nixtlats\timegpt.py in wrapper(self, *args, **kwargs)
    663                 f'supported models: {", ".join(self.supported_models)}'
    664             )
--> 665         return func(self, *args, **kwargs)
    666 
    667     return wrapper

~\anaconda3\lib\site-packages\nixtlats\timegpt.py in wrapper(self, num_partitions, **kwargs)
    681     def wrapper(self, num_partitions, **kwargs):
    682         if num_partitions is None or num_partitions == 1:
--> 683             return func(self, **kwargs, num_partitions=1)
    684         df = kwargs.pop("df")
    685         X_df = kwargs.pop("X_df", None)

~\anaconda3\lib\site-packages\nixtlats\timegpt.py in _forecast(self, df, h, freq, id_col, time_col, target_col, X_df, level, quantiles, finetune_steps, finetune_loss, clean_ex_first, validate_token, add_history, date_features, date_features_to_one_hot, model, num_partitions)
    821             max_wait_time=self.max_wait_time,
    822         )
--> 823         fcst_df = timegpt_model.forecast(df=df, X_df=X_df, add_history=add_history)
    824         self.weights_x = timegpt_model.weights_x
    825         return fcst_df

~\anaconda3\lib\site-packages\nixtlats\timegpt.py in forecast(self, df, X_df, add_history)
    554             fitted_df = fitted_df.drop(columns="y")
    555             fcst_df = pd.concat([fitted_df, fcst_df]).sort_values(["unique_id", "ds"])
--> 556         fcst_df = self.transform_outputs(fcst_df, level_to_quantiles=True)
    557         return fcst_df
    558 

~\anaconda3\lib\site-packages\nixtlats\timegpt.py in transform_outputs(self, fcst_df, level_to_quantiles)
    246                     col = f"TimeGPT-{hi_or_lo}-{lv}"
    247                 q_col = f"TimeGPT-q-{int(q * 100)}"
--> 248                 fcst_df[q_col] = fcst_df[col].values
    249                 cols.append(q_col)
    250             fcst_df = fcst_df[cols]

~\anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
   3503             if self.columns.nlevels > 1:
   3504                 return self._getitem_multilevel(key)
-> 3505             indexer = self.columns.get_loc(key)
   3506             if is_integer(indexer):
   3507                 indexer = [indexer]

~\anaconda3\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
   3629                 return self._engine.get_loc(casted_key)
   3630             except KeyError as err:
-> 3631                 raise KeyError(key) from err
   3632             except TypeError:
   3633                 # If we have a listlike key, _check_indexing_error will raise

KeyError: 'TimeGPT-lo-20'