class_docstr: Baseline model that sets the forecast equal to the historic average or quantile.
  
  Other Parameters
  ----------------
  n_jobs : int or float, default = 0.5
      Number of CPU cores used to fit the models in parallel.
      When set to a float between 0.0 and 1.0, that fraction of available CPU cores is used.
      When set to a positive integer, that many cores are used.
      When set to -1, all CPU cores are used.
  max_ts_length : Optional[int], default = None
      If not None, only the last ``max_ts_length`` time steps of each time series will be used to train the model.
      This significantly speeds up fitting and usually leads to no change in accuracy.
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code: def _predict_with_local_model(
      self,
      time_series: pd.Series,
      local_model_args: dict,
  ) -> pd.DataFrame:
      agg_functions = ["mean"] + [
          _get_quantile_function(q) for q in self.quantile_levels
      ]
      stats_marginal = time_series.agg(agg_functions)
      stats_repeated = np.tile(
          stats_marginal.values, [self.prediction_length, 1]
      )
      return pd.DataFrame(stats_repeated, columns=stats_marginal.index)
  
file_path: autogluon/timeseries/src/autogluon/timeseries/models/local/naive.py
imports: from typing import Callable
  import numpy
  import pandas
  from autogluon.timeseries.models.local.abstract_local_model import AbstractLocalModel, seasonal_naive_forecast
label_desc: Use image node_id 1 for calling the AverageModel obj's underlying member method code with example usage: obj._predict_with_local_model(time_series, local_model_args) and returns: pd