class_docstr: Baseline model that sets the forecast equal to the last observed value from the same season.
  
  Quantiles are obtained by assuming that the residuals follow zero-mean normal distribution, scale of which is
  estimated from the empirical distribution of the residuals.
  As described in https://otexts.com/fpp3/prediction-intervals.html
  
  
  Other Parameters
  ----------------
  seasonal_period : int or None, default = None
      Number of time steps in a complete seasonal cycle for seasonal models. For example, 7 for daily data with a
      weekly cycle or 12 for monthly data with an annual cycle.
      When set to None, seasonal_period will be inferred from the frequency of the training data. Can also be
      specified manually by providing an integer > 1.
      If seasonal_period (inferred or provided) is equal to 1, will fall back to Naive forecast.
      Seasonality will also be disabled, if the length of the time series is < seasonal_period.
  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.
class_docstr_tok: ["Baseline","model","that","sets","the","forecast","equal","to","the","last","observed","value","from","the","same","season",".","Quantiles","are","obtained","by","assuming","that","the","residuals","follow","zero-mean","normal","distribution",",","scale","of","which","is","estimated","from","the","empirical","distribution","of","the","residuals",".","As","described","in","https",":","\/\/otexts.com\/fpp3\/prediction-intervals.html","Other","Parameters","--","--","--","--","--","--","--","--","seasonal_period",":","int","or","None",",","default","=","None","Number","of","time","steps","in","a","complete","seasonal","cycle","for","seasonal","models",".","For","example",",","7","for","daily","data","with","a","weekly","cycle","or","12","for","monthly","data","with","an","annual","cycle",".","When","set","to","None",",","seasonal_period","will","be","inferred","from","the","frequency","of","the","training","data",".","Can","also","be","specified","manually","by","providing","an","integer",">","1",".","If","seasonal_period","(","inferred","or","provided",")","is","equal","to","1",",","will","fall","back","to","Naive","forecast",".","Seasonality","will","also","be","disabled",",","if","the","length","of","the","time","series","is","<","seasonal_period",".","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","."]
code: def _predict_with_local_model(
      self,
      time_series: np.ndarray,
      local_model_args: dict,
  ) -> pd.DataFrame:
      return seasonal_naive_forecast(
          target=time_series.values.ravel(),
          prediction_length=self.prediction_length,
          quantile_levels=self.quantile_levels,
          seasonal_period=local_model_args["seasonal_period"],
      )
  
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 SeasonalNaiveModel obj's underlying member method code with example usage: obj._predict_with_local_model(time_series, local_model_args) and returns: seasonal_naive_forecast