class_docstr: Implementation of the classic local statistical model ETS (Error, Trend, Seasonal) for forecasting.
class_docstr_tok: ["Implementation","of","the","classic","local","statistical","model","ETS","(","Error",",","Trend",",","Seasonal",")","for","forecasting","."]
code: def _instantiate_model(self, data):
      with warnings.catch_warnings():
          warnings.simplefilter("ignore")
          return ETSModel(
              data,
              error=self.error,
              trend=self.trend,
              seasonal=None
              if self.seasonal_periods is None
              else self.seasonal,
              damped_trend=self.damped_trend,
              seasonal_periods=self.seasonal_periods,
          )
  
imports: import logging
  from typing import List, Tuple, Union
  import warnings
  import numpy
  import pandas
  from statsmodels.tsa.exponential_smoothing.ets import ETSModel
  from merlion.models.automl.seasonality import SeasonalityModel
  from merlion.models.forecast.base import ForecasterBase, ForecasterConfig
  from merlion.transform.resample import TemporalResample
  from merlion.utils import UnivariateTimeSeries, to_pd_datetime
label_desc: Use image node_id 10 for calling the ETS obj's underlying member method code with example usage: obj._instantiate_model(data) and returns: ETSModel