text: Title: Pytorch-Forecasting imports deprecated property from transient dependency on numpy
  Body: - PyTorch-Forecasting version: 0.10.3
  - PyTorch version: 1.12.1
  - Python version: 3.8
  - Operating System: Ubuntu 20.04
  
  ### Expected behavior
  
  I created a simple `TimeSeriesDataset` without specifying an explicit `target_normalizer`. I expected it to simply create a default normalizer deduced from the other arguments as explained in the documentation.
  
  ### Actual behavior
  
  An exception was raised of the type `AttributeError` by the `numpy` package. The cause is that aliases like `numpy.float` and `numpy.int` have been deprecated as of numpy `1.20` which has been out for almost two years. The deprecation is explained [here](https://numpy.org/doc/stable/release/1.20.0-notes.html#deprecations). This dependency on `numpy<=1.19` is not specified by `pytorch-forecasting` as described in #1130 .
  
  ### Code to reproduce the problem
  
  Create an environment with the above versions and install numpy >= 1.20. Then run the first few cell's of the tutorial in the documentation for TFT: [https://pytorch-forecasting.readthedocs.io/en/stable/tutorials/stallion.html](https://pytorch-forecasting.readthedocs.io/en/stable/tutorials/stallion.html) 
  
  <details><summary>STACKTRACE</summary>
  <p>
  
  #### The stacktrace from a simple `TimeSeriesDataSet` creation.
  
  ```python
  
  ---------------------------------------------------------------------------
  NotFittedError                            Traceback (most recent call last)
  File /opt/conda/envs/leap-dsr-rd/lib/python3.8/site-packages/pytorch_forecasting/data/timeseries.py:753, in TimeSeriesDataSet._preprocess_data(self, data)
      752 try:
  --> 753     check_is_fitted(self.target_normalizer)
      754 except NotFittedError:
  
  File /opt/conda/envs/leap-dsr-rd/lib/python3.8/site-packages/sklearn/utils/validation.py:1380, in check_is_fitted(estimator, attributes, msg, all_or_any)
     1379 if not fitted:
  -> 1380     raise NotFittedError(msg % {"name": type(estimator).__name__})
  
  NotFittedError: This GroupNormalizer instance is not fitted yet. Call 'fit' with appropriate arguments before using this estimator.
  
  During handling of the above exception, another exception occurred:
  
  AttributeError                            Traceback (most recent call last)
  Cell In[10], line 1
  ----> 1 training = TimeSeriesDataSet(
        2     df_h[df_h["local_hour_start"].dt.year == 2021],
        3     group_ids=["meter_id"],
        4     time_idx="local_hour_idx",
        5     target="energy_kwh",
        6     target_normalizer=GroupNormalizer(groups=["meter_id"]),
        7     max_encoder_length=24 * 7,
        8     min_prediction_length=3,  # One hour plus 2 buffer hours
        9     max_prediction_length=7,  # Five hours plus 2 buffer hours
       10     time_varying_unknown_categoricals=[],
       11     time_varying_unknown_reals=["energy_kwh"],
       12     time_varying_known_categoricals=["is_event_hour"],
       13     time_varying_known_reals=[],
       14 )
       16 # validation = TimeSeriesDataSet.from_dataset(training, df_h, predict=True, stop_randomization=True)
  
  File /opt/conda/envs/leap-dsr-rd/lib/python3.8/site-packages/pytorch_forecasting/data/timeseries.py:476, 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)
      473 data = data.sort_values(self.group_ids + [self.time_idx])
      475 # preprocess data
  --> 476 data = self._preprocess_data(data)
      477 for target in self.target_names:
      478     assert target not in self.scalers, "Target normalizer is separate and not in scalers."
  
  File /opt/conda/envs/leap-dsr-rd/lib/python3.8/site-packages/pytorch_forecasting/data/timeseries.py:758, in TimeSeriesDataSet._preprocess_data(self, data)
      756     self.target_normalizer.fit(data[self.target])
      757 elif isinstance(self.target_normalizer, (GroupNormalizer, MultiNormalizer)):
  --> 758     self.target_normalizer.fit(data[self.target], data)
      759 else:
      760     self.target_normalizer.fit(data[self.target])
  
  File /opt/conda/envs/leap-dsr-rd/lib/python3.8/site-packages/pytorch_forecasting/data/encoders.py:771, in GroupNormalizer.fit(self, y, X)
      760 """
      761 Determine scales for each group
      762 
     (...)
      768     self
      769 """
      770 y = self.preprocess(y)
  --> 771 eps = np.finfo(np.float).eps
      772 if len(self.groups) == 0:
      773     assert not self.scale_by_group, "No groups are defined, i.e. `scale_by_group=[]`"
  
  File /opt/conda/envs/leap-dsr-rd/lib/python3.8/site-packages/numpy/__init__.py:284, in __getattr__(attr)
      281     from .testing import Tester
      282     return Tester
  --> 284 raise AttributeError("module {!r} has no attribute "
      285                      "{!r}".format(__name__, attr))
  
  AttributeError: module 'numpy' has no attribute 'float'
  
  ```
  
  </p>
  </details>