class_docstr: Naïve 2 forecaster as described in the M4 Competition Guide:
  http://www.unic.ac.cy/test/wp-content/uploads/sites/2/2018/09/M4-Competitors-Guide.pdf
  
  The Python analogue implementation to:
  https://github.com/Mcompetitions/M4-methods/blob/master/Benchmarks%20and%20Evaluation.R#L118
  
  Parameters
  ----------
  prediction_length
      Number of time points to predict
  season_length
      Length of the seasonality pattern of the input data
class_docstr_tok: ["Na\u00efve","2","forecaster","as","described","in","the","M4","Competition","Guide",":","http",":","\/\/www.unic.ac.cy\/test\/wp-content\/uploads\/sites\/2\/2018\/09\/M4-Competitors-Guide.pdf","The","Python","analogue","implementation","to",":","https",":","\/\/github.com\/Mcompetitions\/M4-methods\/blob\/master\/Benchmarks","%","20and","%","20Evaluation.R","#","L118","Parameters","--","--","--","--","--","prediction_length","Number","of","time","points","to","predict","season_length","Length","of","the","seasonality","pattern","of","the","input","data"]
code: def __init__(
      self,
      prediction_length: int,
      season_length: int,
  ) -> None:
      super().__init__(prediction_length=prediction_length)
  
      assert (
          season_length > 0
      ), "The value of `season_length` should be > 0"
  
      self.prediction_length = prediction_length
      self.season_length = season_length
  
file_path: gluonts/src/gluonts/ext/naive_2/_predictor.py
imports: import numpy
  import statsmodels.api
  from gluonts.core.component import validated
  from gluonts.dataset.common import DataEntry
  from gluonts.dataset.util import forecast_start
  from gluonts.model.forecast import Forecast, SampleForecast
  from gluonts.model.predictor import RepresentablePredictor
label_desc: Use image node_id 1 to create a new Naive2Predictor object from inherited base classes: RepresentablePredictor with example: obj = Naive2Predictor(prediction_length, season_length)