---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Input In [93], in <cell line: 2>()
      1 #turn the pandas dataframe into a Time Series Dataset
----> 2 training = TimeSeriesDataSet(
      3   data=youtube_df[lambda x: x.time_idx <= training_cutoff],
      4   time_idx='time_idx',
      5   target=['HIBERN8_EXIT','READ','SECURITY PROTOCOL IN','SECURITY PROTOCOL OUT','SYNCHRONIZE CACHE','UNMAP','WRITE'],
      6   group_ids=['group'],
      7   min_encoder_length=7,
      8   max_encoder_length=35,
      9   static_categoricals=['group'],
     10   static_reals=[],
     11   time_varying_known_reals=[],
     12   time_varying_unknown_reals=['timestamp','LBA','SIZE','TAG','Latency (ms)','HIBERN8_EXIT','READ','SECURITY PROTOCOL IN','SECURITY PROTOCOL OUT','SYNCHRONIZE CACHE','UNMAP','WRITE'],
     13   target_normalizer=MultiNormalizer([EncoderNormalizer(), TorchNormalizer()]),
     21   time_varying_known_categoricals=[],
     22   time_varying_unknown_categoricals=[],
     23   min_prediction_length=7,
     24   max_prediction_length=10000,
     25   add_relative_time_idx=True,
     26   add_target_scales=True,
     27   add_encoder_length=True,
     28   allow_missing_timesteps=True
     29 )
     30 validation = TimeSeriesDataSet.from_dataset(training, youtube_df, predict=True, stop_randomization=True)
     32 # create dataloaders for model

File ~/.local/lib/python3.8/site-packages/pytorch_forecasting/data/timeseries.py:434, in TimeSeriesDataSet.__init__(self, data, time_idx, target, group_ids, weight, max_encoder_length, min_encoder_length, min_prediction_idx, min_prediction_length, max_prediction_length, static_categoricals, static_reals, time_varying_known_categoricals, time_varying_known_reals, time_varying_unknown_categoricals, time_varying_unknown_reals, variable_groups, constant_fill_strategy, allow_missing_timesteps, lags, add_relative_time_idx, add_target_scales, add_encoder_length, target_normalizer, categorical_encoders, scalers, randomize_length, predict_mode)
    431 data = data.sort_values(self.group_ids + [self.time_idx])
    433 # preprocess data
--> 434 data = self._preprocess_data(data)
    435 for target in self.target_names:
    436     assert target not in self.scalers, "Target normalizer is separate and not in scalers."

File ~/.local/lib/python3.8/site-packages/pytorch_forecasting/data/timeseries.py:740, in TimeSeriesDataSet._preprocess_data(self, data)
    737 transformed, scales = self.target_normalizer.transform(data[self.target], data, return_norm=True)
    739 for idx, target in enumerate(self.target_names):
--> 740     data[target] = transformed[idx]
    742     if isinstance(self.target_normalizer[idx], NaNLabelEncoder):
    743         # overwrite target because it requires encoding (continuous targets should not be normalized)
    744         data[f"__target__{target}"] = data[target]

IndexError: list index out of range