class_docstr: Non-Parametric Time Series Forecaster.
  
  This models is especially well suited for forecasting sparse or intermittent time series with many zero values.
  
  Based on `gluonts.model.npts.NPTSPredictor <https://ts.gluon.ai/stable/api/gluonts/gluonts.model.npts.html>`_.
  See GluonTS documentation for more information about the model.
  
  Other Parameters
  ----------------
  kernel_type : {"exponential", "uniform"}, default = "exponential"
      Kernel used by the model.
  exp_kernel_weights : float, default = 1.0
      Scaling factor used in the exponential kernel.
  use_seasonal_model : bool, default = True
      Whether to use the seasonal variant of the model.
  num_samples : int, default = 100
      Number of samples generated by the forecast.
  num_default_time_features : int, default = 1
      Number of time features used by seasonal model.
  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 = 2500
      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.
code: def _update_local_model_args(self, local_model_args: dict) -> dict:
      local_model_args = super()._update_local_model_args(
          local_model_args
      )
      local_model_args.setdefault("num_samples", 100)
      local_model_args.setdefault("num_default_time_features", 1)
      return local_model_args
  
file_path: autogluon/timeseries/src/autogluon/timeseries/models/local/npts.py
imports: import numpy
  import pandas
  from autogluon.timeseries.models.local.abstract_local_model import AbstractLocalModel
  from autogluon.timeseries.utils.datetime import get_time_features_for_frequency
  from autogluon.timeseries.utils.forecast import get_forecast_horizon_index_single_time_series
label_desc: Use image node_id 1 for calling the NPTSModel obj's underlying member method code with example usage: obj._update_local_model_args(local_model_args) and returns: local_model_args