class_docstr: Dynamic Factor Forecaster.
  
  Direct interface for `statsmodels.tsa.statespace.dynamic_factor`
  
  Parameters
  ----------
  k_factors : int
      The number of unobserved factors.
  factor_order : int
      The order of vector autoregression followed by factors.
  error_cov_type : {'scalar','diagonal','unstructured'} ,default = 'diagonal'
      The structure of the covariance matrix of the observation error term, where
      "unstructured" puts no restrictions on the matrix, "diagonal" requires it
      to be any diagonal matrix (uncorrelated errors), and "scalar" requires it
      to be a scalar times the identity matrix.
  error_order : int , default = 0
      The order of the vector autoregression followed by the observation error
      component. Default is None, corresponding to white noise errors.
  error_var : bool , default = False , optional
      Whether or not to model the errors jointly via a vector autoregression,
       rather than as individual autoregression.
  enforce_stationarity : bool default = True
      Whether or not to model the AR parameters to enforce stationarity in the
       autoregressive component of the model.
  start_params :array_like ,default = None
      Initial guess of the solution for the loglikelihood maximization.
  transformed : bool, default = True
      Whether or not start_params is already transformed.
  includes_fixed : bool , default = False
      If parameters were previously fixed with the fix_params method, this argument
       describes whether or not start_params also includes the fixed parameters,
        in addition to the free parameters.
  cov_type : {'opg','oim','approx','robust','robust_approx','none'},default = 'opg'
      'opg' for the outer product of gradient estimator
      'oim' for the observed information matrix estimator, calculated
      using the method of Harvey (1989)
      'approx' for the observed information matrix estimator, calculated using
       a numerical approximation of the Hessian matrix.
      'robust' for an approximate (quasi-maximum likelihood) covariance matrix
       that may be valid even in the presence of some misspecifications.
        Intermediate calculations use the 'oim' method.
      'robust_approx' is the same as 'robust' except that the intermediate
      calculations use the 'approx' method.
      'none' for no covariance matrix calculation
  cov_kwds :dict or None , default = None
      'approx_complex_step' : bool, optional - If True, numerical approximations are
       computed using complex-step methods. If False, numerical approximations are
       computed using finite difference methods. Default is True.
      'approx_centered' : bool, optional - If True, numerical approximations computed
      using finite difference methods use a centered approximation. Default is False.
  method : str , 'lbfgs'
      'newton' for Newton-Raphson
      'nm' for Nelder-Mead
      'bfgs' for Broyden-Fletcher-Goldfarb-Shanno (BFGS)
      'lbfgs' for limited-memory BFGS with optional box constraints
      'powell' for modified Powell's method
      'cg' for conjugate gradient
      'ncg' for Newton-conjugate gradient
      'basinhopping' for global basin-hopping solver
  maxiter : int , optional ,default = 50
      The maximum number of iterations to perform.
  full_output : bool , default = 1
      Set to True to have all available output in the
      Results object's mle_retvals attribute.
      The output is dependent on the solver.
  disp : bool ,   default = 5
      Set to True to print convergence messages.
  callback : callable callback(xk) , default = None
      Called after each iteration, as callback(xk),
      where xk is the current parameter vector.
  return_params : bool ,default = False
      Whether or not to return only the array of maximizing parameters.
  optim_score : {'harvey','approx'} , default = None
      The method by which the score vector is calculated. 'harvey' uses the method
      from Harvey (1989), 'approx' uses either finite difference or
      complex step differentiation depending upon the value of optim_complex_step,
      and None uses the built-in gradient approximation of the optimizer.
  optim_complex_step : bool , default = True
      Whether or not to use complex step differentiation
      when approximating the score; if False, finite difference approximation is used.
  optim_hessian : {'opg','oim','approx'} , default = None
      'opg' uses outer product of gradients, 'oim' uses the information
      matrix formula from Harvey (1989), and 'approx' uses numerical approximation.
  low_memory : bool , default = False
      If set to True, techniques are applied to substantially reduce memory usage.
      If used, some features of the results object will not be available
      (including smoothed results and in-sample prediction),
      although out-of-sample forecasting is possible.
  
  See Also
  --------
  statsmodels.tsa.statespace.dynamic_factor.DynamicFactor
  statsmodels.tsa.statespace.dynamic_factor.DynamicFactorResults
  
  References
  ----------
  .. [1] Lütkepohl, Helmut. 2007. New Introduction to Multiple Time Series Analysis.
  Berlin: Springer.
  
  Examples
  --------
  >>> from sktime.utils._testing.series import _make_series
  >>> from sktime.forecasting.dynamic_factor import DynamicFactor
  >>> y = _make_series(n_columns=4)
  >>> forecaster = DynamicFactor()  # doctest: +SKIP
  >>> forecaster.fit(y)  # doctest: +SKIP
  DynamicFactor(...)
  >>> y_pred = forecaster.predict(fh=[1,2,3])  # doctest: +SKIP
class_docstr_tok: ["Dynamic","Factor","Forecaster",".","Direct","interface","for","`","statsmodels.tsa.statespace.dynamic_factor","`","Parameters","--","--","--","--","--","k_factors",":","int","The","number","of","unobserved","factors",".","factor_order",":","int","The","order","of","vector","autoregression","followed","by","factors",".","error_cov_type",":","{","'scalar","'",",","'diagonal","'",",","'unstructured","'","}",",","default","=","'diagonal'","The","structure","of","the","covariance","matrix","of","the","observation","error","term",",","where","``","unstructured","''","puts","no","restrictions","on","the","matrix",",","``","diagonal","''","requires","it","to","be","any","diagonal","matrix","(","uncorrelated","errors",")",",","and","``","scalar","''","requires","it","to","be","a","scalar","times","the","identity","matrix",".","error_order",":","int",",","default","=","0","The","order","of","the","vector","autoregression","followed","by","the","observation","error","component",".","Default","is","None",",","corresponding","to","white","noise","errors",".","error_var",":","bool",",","default","=","False",",","optional","Whether","or","not","to","model","the","errors","jointly","via","a","vector","autoregression",",","rather","than","as","individual","autoregression",".","enforce_stationarity",":","bool","default","=","True","Whether","or","not","to","model","the","AR","parameters","to","enforce","stationarity","in","the","autoregressive","component","of","the","model",".","start_params",":","array_like",",","default","=","None","Initial","guess","of","the","solution","for","the","loglikelihood","maximization",".","transformed",":","bool",",","default","=","True","Whether","or","not","start_params","is","already","transformed",".","includes_fixed",":","bool",",","default","=","False","If","parameters","were","previously","fixed","with","the","fix_params","method",",","this","argument","describes","whether","or","not","start_params","also","includes","the","fixed","parameters",",","in","addition","to","the","free","parameters",".","cov_type",":","{","'opg","'",",","'oim","'",",","'approx","'",",","'robust","'",",","'robust_approx","'",",","'none","'","}",",","default","=","'opg'","'opg","'","for","the","outer","product","of","gradient","estimator","'oim","'","for","the","observed","information","matrix","estimator",",","calculated","using","the","method","of","Harvey","(","1989",")","'approx","'","for","the","observed","information","matrix","estimator",",","calculated","using","a","numerical","approximation","of","the","Hessian","matrix",".","'robust","'","for","an","approximate","(","quasi-maximum","likelihood",")","covariance","matrix","that","may","be","valid","even","in","the","presence","of","some","misspecifications",".","Intermediate","calculations","use","the","'oim","'","method",".","'robust_approx","'","is","the","same","as","'robust","'","except","that","the","intermediate","calculations","use","the","'approx","'","method",".","'none","'","for","no","covariance","matrix","calculation","cov_kwds",":","dict","or","None",",","default","=","None","'approx_complex_step","'",":","bool",",","optional","-","If","True",",","numerical","approximations","are","computed","using","complex-step","methods",".","If","False",",","numerical","approximations","are","computed","using","finite","difference","methods",".","Default","is","True",".","'approx_centered","'",":","bool",",","optional","-","If","True",",","numerical","approximations","computed","using","finite","difference","methods","use","a","centered","approximation",".","Default","is","False",".","method",":","str",",","'lbfgs'","'newton","'","for","Newton-Raphson","'nm","'","for","Nelder-Mead","'bfgs","'","for","Broyden-Fletcher-Goldfarb-Shanno","(","BFGS",")","'lbfgs","'","for","limited-memory","BFGS","with","optional","box","constraints","'powell","'","for","modified","Powell","'s","method","'cg","'","for","conjugate","gradient","'ncg","'","for","Newton-conjugate","gradient","'basinhopping","'","for","global","basin-hopping","solver","maxiter",":","int",",","optional",",","default","=","50","The","maximum","number","of","iterations","to","perform",".","full_output",":","bool",",","default","=","1","Set","to","True","to","have","all","available","output","in","the","Results","object","'s","mle_retvals","attribute",".","The","output","is","dependent","on","the","solver",".","disp",":","bool",",","default","=","5","Set","to","True","to","print","convergence","messages",".","callback",":","callable","callback","(","xk",")",",","default","=","None","Called","after","each","iteration",",","as","callback","(","xk",")",",","where","xk","is","the","current","parameter","vector",".","return_params",":","bool",",","default","=","False","Whether","or","not","to","return","only","the","array","of","maximizing","parameters",".","optim_score",":","{","'harvey","'",",","'approx","'","}",",","default","=","None","The","method","by","which","the","score","vector","is","calculated",".","'harvey","'","uses","the","method","from","Harvey","(","1989",")",",","'approx","'","uses","either","finite","difference","or","complex","step","differentiation","depending","upon","the","value","of","optim_complex_step",",","and","None","uses","the","built-in","gradient","approximation","of","the","optimizer",".","optim_complex_step",":","bool",",","default","=","True","Whether","or","not","to","use","complex","step","differentiation","when","approximating","the","score",";","if","False",",","finite","difference","approximation","is","used",".","optim_hessian",":","{","'opg","'",",","'oim","'",",","'approx","'","}",",","default","=","None","'opg","'","uses","outer","product","of","gradients",",","'oim","'","uses","the","information","matrix","formula","from","Harvey","(","1989",")",",","and","'approx","'","uses","numerical","approximation",".","low_memory",":","bool",",","default","=","False","If","set","to","True",",","techniques","are","applied","to","substantially","reduce","memory","usage",".","If","used",",","some","features","of","the","results","object","will","not","be","available","(","including","smoothed","results","and","in-sample","prediction",")",",","although","out-of-sample","forecasting","is","possible",".","See","Also","--","--","--","--","statsmodels.tsa.statespace.dynamic_factor.DynamicFactor","statsmodels.tsa.statespace.dynamic_factor.DynamicFactorResults","References","--","--","--","--","--","..","[","1","]","L\u00fctkepohl",",","Helmut",".","2007",".","New","Introduction","to","Multiple","Time","Series","Analysis",".","Berlin",":","Springer",".","Examples","--","--","--","--",">",">",">","from","sktime.utils._testing.series","import","_make_series",">",">",">","from","sktime.forecasting.dynamic_factor","import","DynamicFactor",">",">",">","y","=","_make_series","(","n_columns=4",")",">",">",">","forecaster","=","DynamicFactor","(",")","#","doctest",":","+SKIP",">",">",">","forecaster.fit","(","y",")","#","doctest",":","+SKIP","DynamicFactor","(","...",")",">",">",">","y_pred","=","forecaster.predict","(","fh=","[","1,2,3","]",")","#","doctest",":","+SKIP"]
docstr: Return testing parameter settings for the estimator.
  
  Parameters
  ----------
  parameter_set : str , default = "default"
      Name of the set of test parameters to return, for use in tests. If no
      special parameters are defined for a value, will return `"default"` set.
      There are currently no reserved values for forecasters.
  
  Returns
  -------
  params :dict or list of dict , default = {}
      Parameters to create testing instances of the class
      Each dict are parameters to construct an "interesting" test instance, i.e.,
      `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
      `create_test_instance` uses the first (or only) dictionary in `params`
docstr_tok: ["Return","testing","parameter","settings","for","the","estimator",".","Parameters","--","--","--","--","--","parameter_set",":","str",",","default","=","``","default","''","Name","of","the","set","of","test","parameters","to","return",",","for","use","in","tests",".","If","no","special","parameters","are","defined","for","a","value",",","will","return","`","``","default","''","`","set",".","There","are","currently","no","reserved","values","for","forecasters",".","Returns","--","--","--","-","params",":","dict","or","list","of","dict",",","default","=","{","}","Parameters","to","create","testing","instances","of","the","class","Each","dict","are","parameters","to","construct","an","``","interesting","''","test","instance",",","i.e.",",","`","MyClass","(","*","*","params",")","`","or","`","MyClass","(","*","*","params","[","i","]",")","`","creates","a","valid","test","instance",".","`","create_test_instance","`","uses","the","first","(","or","only",")","dictionary","in","`","params","`"]
code: def get_test_params(cls, parameter_set="default"):
      """Return testing parameter settings for the estimator.
  
      Parameters
      ----------
      parameter_set : str , default = "default"
          Name of the set of test parameters to return, for use in tests. If no
          special parameters are defined for a value, will return `"default"` set.
          There are currently no reserved values for forecasters.
  
      Returns
      -------
      params :dict or list of dict , default = {}
          Parameters to create testing instances of the class
          Each dict are parameters to construct an "interesting" test instance, i.e.,
          `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
          `create_test_instance` uses the first (or only) dictionary in `params`
      """
      params1 = {"k_factors": 1, "factor_order": 1}
      params2 = {"maxiter": 25, "low_memory": True}
  
      return [params1, params2]
  
file_path: sktime/sktime/forecasting/dynamic_factor.py
imports: import numpy
  import pandas
  from sktime.forecasting.base.adapters import _StatsModelsAdapter
label_desc: Use image node_id 8 for calling the DynamicFactor obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list