class_docstr: Forecaster that plays back known or prescribed values as forecasts.
  
  Takes a data set of "known future values" to produces these in the sktime interface.
  
  Common use cases for this forecaster:
  
  * as a dummy or naive forecaster with a known baseline expectation
  * as a forecaster with (non-naive) expert forecasts, "known" values as per expert
  * as a counterfactual in benchmarking experiments, "what if we knew the truth"
  * to pass forecast data values in a composite used for postprocessing,
    e.g., in combination with ReconcilerForecaster for an isolated reconciliation step
  
  When forecasting, uses `pandas.DataFrame.reindex` under the hood to obtain predicted
  values from `y_known`. Parameters other than `y_known` are directly passed
  on to `pandas.DataFrame.reindex`.
  
  Parameters
  ----------
  y_known : pd.DataFrame or pd.Series in one of the sktime compatible data formats
      should contain known values that the forecaster will replay in predict
      can also be in a non-pandas sktime data format, will then be coerced to pandas
  method : str or None, optional, default=None
      one of {None, 'backfill'/'bfill', 'pad'/'ffill', 'nearest'}
      method to use for imputing indices at which forecasts are unavailable in y_known
  fill_value : scalar, optional, default=np.NaN
      value to use for any missing values (e.g., if `method` is None)
  limit : int, optional, default=None=infinite
      maximum number of consecutive elements to bfill/ffill if `method=bfill`/`ffill`
  
  Examples
  --------
  >>> y_known = pd.DataFrame(range(100))
  >>> y_train = y_known[:24]
  >>>
  >>> from sktime.forecasting.dummy import ForecastKnownValues
  >>>
  >>> fcst = ForecastKnownValues(y_known)
  >>> fcst.fit(y_train, fh=[1, 2, 3])
  ForecastKnownValues(...)
  
  The forecast "plays back" the known/prescribed values from y_known
  
  >>> y_pred = fcst.predict()
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`
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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`
      """
      from sktime.utils._testing.series import _make_series
  
      y = _make_series(n_columns=1)
      y2 = _make_series(n_columns=2, n_timepoints=15, index_type="int")
  
      params1 = {"y_known": y, "fill_value": 42}
      params2 = {
          "y_known": y2,
          "method": "ffill",
          "limit": 3,
          "fill_value": 42,
      }
  
      return [params1, params2]
  
file_path: sktime/sktime/forecasting/dummy.py
imports: import pandas
  from sktime.datatypes import convert_to
  from sktime.forecasting.base import BaseForecaster
label_desc: Use image node_id 4 for calling the ForecastKnownValues obj's underlying member method code with example usage: obj.get_test_params(cls, parameter_set) and returns: list