class_docstr: Directly interfaces ARCH models from python package arch.
  
  ARCH models are a popular class of volatility models that use observed values of
  returns or residuals as volatility shocks to forecast the volatility in high
  frequency time series data..
  
  A complete ARCH model is divided into three components:
      a mean model, e.g., a constant mean or an ARX;
      a volatility process, e.g., a GARCH or an EGARCH process; and
      a distribution for the standardized residuals.
  
  Parameters
  ----------
  mean : str, optional
      Name of the mean model.  Currently supported options are: 'Constant',
      'Zero', 'LS', 'AR', 'ARX', 'HAR' and  'HARX'
  lags : int or list (int), optional
      Either a scalar integer value indicating lag length or a list of
      integers specifying lag locations.
  vol : str, optional
      Name of the volatility model.  Currently supported options are:
      'GARCH' (default), 'ARCH', 'EGARCH', 'FIARCH' and 'HARCH'
  p : int, optional
      Lag order of the symmetric innovation
  o : int, optional
      Lag order of the asymmetric innovation
  q : int, optional
      Lag order of lagged volatility or equivalent
  power : float, optional
      Power to use with GARCH and related models
  dist : int, optional
      Name of the error distribution.  Currently supported options are:
          * Normal: 'normal', 'gaussian' (default)
          * Students's t: 't', 'studentst'
          * Skewed Student's t: 'skewstudent', 'skewt'
          * Generalized Error Distribution: 'ged', 'generalized error"
  hold_back : int
      Number of observations at the start of the sample to exclude when
      estimating model parameters.  Used when comparing models with different
      lag lengths to estimate on the common sample.
  rescale : bool
      Flag indicating whether to automatically rescale data if the scale
      of the data is likely to produce convergence issues when estimating
      model parameters. If False, the model is estimated on the data without
      transformation.  If True, than y is rescaled and the new scale is
      reported in the estimation results.
  update_freq : int, optional
      Frequency of iteration updates.  Output is generated every
      update_freq iterations. Set to 0 to disable iterative output.
  disp : {bool, "off", "final"}
      Either 'final' to print optimization result or 'off' to display
      nothing. If using a boolean, False is "off" and True is "final"
  starting_values : np.ndarray, optional
      Array of starting values to use.  If not provided, starting values
      are constructed by the model components.
  cov_type : str, optional
      Estimation method of parameter covariance.  Supported options are
      'robust', which does not assume the Information Matrix Equality
      holds and 'classic' which does.  In the ARCH literature, 'robust'
      corresponds to Bollerslev-Wooldridge covariance estimator.
  show_warning : bool, optional
      Flag indicating whether convergence warnings should be shown
  first_obs : {int, str, datetime, Timestamp}
      First observation to use when estimating model
  last_obs : {int, str, datetime, Timestamp}
      Last observation to use when estimating model
  tol : float, optional
      Tolerance for termination.
  options : dict, optional
      Options to pass to scipy.optimize.minimize.  Valid entries
      include 'ftol', 'eps', 'disp', and 'maxiter'.
  backcast : {float, np.ndarray}, optional
      Value to use as backcast. Should be measure \\sigma^2_0
      since model-specific non-linear transformations are applied to
      value before computing the variance recursions.
  params : {np.ndarray, Series}
      Parameters required to forecast. Must be identical in shape to the
      parameters computed by fitting the model.
  start : {int, datetime, Timestamp, str}, optional
      An integer, datetime or str indicating the first observation to produce the
      forecast for. Datetimes can only be used with pandas inputs that have a
      datetime index. Strings must be convertible to a date time, such as in
      '1945-01-01'.
  align : str, optional
      Either 'origin' or 'target'. When set of 'origin', the t-th row of forecast
      contains the forecasts for t+1, t+2, ..., t+h. When set to 'target', the
      t-th row contains the 1-step ahead forecast from time t-1, the 2 step from
      time t-2, ..., and the h-step from time t-h. 'target' simplified computing
      forecast errors since the realization and h-step forecast are aligned.
  method : {'analytic', 'simulation', 'bootstrap'}
      Method to use when producing the forecast. The default is analytic.
      The method only affects the variance forecast generation. Not all volatility
      models support all methods.
      In particular, volatility models that do not evolve in squares such as
      EGARCH or TARCH
      do not support the 'analytic' method for horizons > 1.
  simulations : int
      Number of simulations to run when computing the forecast using either
      simulation or bootstrap.
  rng : callable, optional
      Custom random number generator to use in simulation-based forecasts.
      Must produce random samples using the syntax rng(size) where size the
      2-element tuple (simulations, horizon).
  random_state : RandomState, optional
      NumPy RandomState instance to use when method is 'bootstrap'
  reindex : bool, optional
      Whether to reindex the forecasts to have the same dimension as the series
      being forecast. Prior to 4.18 this was the default. As of 4.19 this is now
      optional. If not provided, a warning is raised about the future change in
      the default which will occur after September 2021.
  
  See Also
  --------
  StatsForecastARCH
  StatsForecastGARCH
  
  References
  ----------
  .. [1] GitHub repository of arch package (soft dependency).
     https://github.com/bashtage/arch
  .. [2] Documentation of arch package (soft dependency). Forecasting Volatility with
     ARCH and it's variants.
     https://arch.readthedocs.io/en/latest/univariate/introduction.html
  
  Examples
  --------
  >>> from sktime.datasets import load_airline  # doctest: +SKIP
  >>> from sktime.forecasting.arch import ARCH  # doctest: +SKIP
  >>> y = load_airline()  # doctest: +SKIP
  >>> forecaster = ARCH()  # doctest: +SKIP
  >>> forecaster.fit(y)  # doctest: +SKIP
  ARCH(...)
  >>> y_pred = forecaster.predict(fh=1)  # doctest: +SKIP
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docstr: Get fitted parameters.
  
  Returns
  -------
  fitted_params : dict
docstr_tok: ["Get","fitted","parameters",".","Returns","--","--","--","-","fitted_params",":","dict"]
code: def _get_fitted_params(self):
      """Get fitted parameters.
  
      Returns
      -------
      fitted_params : dict
      """
      self.check_is_fitted()
      fitted_params = dict(self._fitted_forecaster.params)
      return fitted_params
  
file_path: sktime/sktime/forecasting/arch/_uarch.py
imports: import itertools
  from collections import OrderedDict
  import numpy
  import pandas
  import scipy.stats
  from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 7 for calling the ARCH obj's underlying member method code with example usage: obj._get_fitted_params() and returns: fitted_params