ForecastKnownValues

class ForecastKnownValues(y_known, method=None, fill_value=None, limit=None)[source]

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_knownpd.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
methodstr 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_valuescalar, optional, default=np.NaN: value to use for any missing values (e.g., if method is None)
limitint, optional, default=None=infinite: maximum number of consecutive elements to bfill / ffill if method='bfill'/'ffill'

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

Examples

>>> import pandas as pd
>>> 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()

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.