class_docstr: Simple Exponential Smoothing
  
  Parameters
  ----------
  endog : array_like
      The time series to model.
  initialization_method : str, optional
      Method for initialize the recursions. One of:
  
      * None
      * 'estimated'
      * 'heuristic'
      * 'legacy-heuristic'
      * 'known'
  
      None defaults to the pre-0.12 behavior where initial values
      are passed as part of ``fit``. If any of the other values are
      passed, then the initial values must also be set when constructing
      the model. If 'known' initialization is used, then `initial_level`
      must be passed, as well as `initial_trend` and `initial_seasonal` if
      applicable. Default is 'estimated'. "legacy-heuristic" uses the same
      values that were used in statsmodels 0.11 and earlier.
  initial_level : float, optional
      The initial level component. Required if estimation method is "known".
      If set using either "estimated" or "heuristic" this value is used.
      This allows one or more of the initial values to be set while
      deferring to the heuristic for others or estimating the unset
      parameters.
  
  See Also
  --------
  ExponentialSmoothing
      Exponential smoothing with trend and seasonal components.
  Holt
      Exponential smoothing with a trend component.
  
  Notes
  -----
  This is a full implementation of the simple exponential smoothing as
  per [1]_.  `SimpleExpSmoothing` is a restricted version of
  :class:`ExponentialSmoothing`.
  
  See the notebook `Exponential Smoothing
  <../examples/notebooks/generated/exponential_smoothing.html>`__
  for an overview.
  
  References
  ----------
  .. [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
      and practice. OTexts, 2014.
docstr: Fit the model
  
  Parameters
  ----------
  smoothing_level : float, optional
      The smoothing_level value of the simple exponential smoothing, if
      the value is set then this value will be used as the value.
  optimized : bool, optional
      Estimate model parameters by maximizing the log-likelihood.
  start_params : ndarray, optional
      Starting values to used when optimizing the fit.  If not provided,
      starting values are determined using a combination of grid search
      and reasonable values based on the initial values of the data.
  initial_level : float, optional
      Value to use when initializing the fitted level.
  use_brute : bool, optional
      Search for good starting values using a brute force (grid)
      optimizer. If False, a naive set of starting values is used.
  use_boxcox : {True, False, 'log', float}, optional
      Should the Box-Cox transform be applied to the data first? If 'log'
      then apply the log. If float then use the value as lambda.
  remove_bias : bool, optional
      Remove bias from forecast values and fitted values by enforcing
      that the average residual is equal to zero.
  method : str, default "L-BFGS-B"
      The minimizer used. Valid options are "L-BFGS-B" (default), "TNC",
      "SLSQP", "Powell", "trust-constr", "basinhopping" (also "bh") and
      "least_squares" (also "ls"). basinhopping tries multiple starting
      values in an attempt to find a global minimizer in non-convex
      problems, and so is slower than the others.
  minimize_kwargs : dict[str, Any]
      A dictionary of keyword arguments passed to SciPy's minimize
      function if method is one of "L-BFGS-B" (default), "TNC",
      "SLSQP", "Powell", or "trust-constr", or SciPy's basinhopping
      or least_squares. The valid keywords are optimizer specific.
      Consult SciPy's documentation for the full set of options.
  
  Returns
  -------
  HoltWintersResults
      See statsmodels.tsa.holtwinters.HoltWintersResults.
  
  Notes
  -----
  This is a full implementation of the simple exponential smoothing as
  per [1].
  
  References
  ----------
  [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
      and practice. OTexts, 2014.
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code: def fit(
      self,
      smoothing_level=None,
      *,
      optimized=True,
      start_params=None,
      initial_level=None,
      use_brute=True,
      use_boxcox=None,
      remove_bias=False,
      method=None,
      minimize_kwargs=None,
  ):
      """
      Fit the model
  
      Parameters
      ----------
      smoothing_level : float, optional
          The smoothing_level value of the simple exponential smoothing, if
          the value is set then this value will be used as the value.
      optimized : bool, optional
          Estimate model parameters by maximizing the log-likelihood.
      start_params : ndarray, optional
          Starting values to used when optimizing the fit.  If not provided,
          starting values are determined using a combination of grid search
          and reasonable values based on the initial values of the data.
      initial_level : float, optional
          Value to use when initializing the fitted level.
      use_brute : bool, optional
          Search for good starting values using a brute force (grid)
          optimizer. If False, a naive set of starting values is used.
      use_boxcox : {True, False, 'log', float}, optional
          Should the Box-Cox transform be applied to the data first? If 'log'
          then apply the log. If float then use the value as lambda.
      remove_bias : bool, optional
          Remove bias from forecast values and fitted values by enforcing
          that the average residual is equal to zero.
      method : str, default "L-BFGS-B"
          The minimizer used. Valid options are "L-BFGS-B" (default), "TNC",
          "SLSQP", "Powell", "trust-constr", "basinhopping" (also "bh") and
          "least_squares" (also "ls"). basinhopping tries multiple starting
          values in an attempt to find a global minimizer in non-convex
          problems, and so is slower than the others.
      minimize_kwargs : dict[str, Any]
          A dictionary of keyword arguments passed to SciPy's minimize
          function if method is one of "L-BFGS-B" (default), "TNC",
          "SLSQP", "Powell", or "trust-constr", or SciPy's basinhopping
          or least_squares. The valid keywords are optimizer specific.
          Consult SciPy's documentation for the full set of options.
  
      Returns
      -------
      HoltWintersResults
          See statsmodels.tsa.holtwinters.HoltWintersResults.
  
      Notes
      -----
      This is a full implementation of the simple exponential smoothing as
      per [1].
  
      References
      ----------
      [1] Hyndman, Rob J., and George Athanasopoulos. Forecasting: principles
          and practice. OTexts, 2014.
      """
      return super().fit(
          smoothing_level=smoothing_level,
          optimized=optimized,
          start_params=start_params,
          initial_level=initial_level,
          use_brute=use_brute,
          remove_bias=remove_bias,
          use_boxcox=use_boxcox,
          method=method,
          minimize_kwargs=minimize_kwargs,
      )
  
imports: from statsmodels.compat.pandas import deprecate_kwarg
  import contextlib
  from typing import Any
  from collections.abc import Hashable, Sequence
  import warnings
  import numpy
  import pandas
  from scipy.optimize import basinhopping, least_squares, minimize
  from scipy.special import inv_boxcox
  from scipy.stats import boxcox
  from statsmodels.tools.validation import array_like, bool_like, dict_like, float_like, int_like, string_like
  from statsmodels.tsa.base.tsa_model import TimeSeriesModel
  from statsmodels.tsa.exponential_smoothing.ets import _initialization_heuristic, _initialization_simple
  from statsmodels.tsa.holtwinters import _exponential_smoothers, _smoothers
  from statsmodels.tsa.holtwinters._exponential_smoothers import HoltWintersArgs
  from statsmodels.tsa.holtwinters._smoothers import to_restricted, to_unrestricted
  from statsmodels.tsa.holtwinters.results import HoltWintersResults, HoltWintersResultsWrapper
  from statsmodels.tsa.tsatools import freq_to_period
label_desc: Use image node_id 2 for calling the SimpleExpSmoothing obj's underlying member method code with example usage: obj.fit(smoothing_level) and returns: super