DummyGlobalForecaster

DummyGlobalForecaster

class DummyGlobalForecaster(strategy='mean')[source]

Dummy global forecaster that predicts mean of pretrain data.

This forecaster implements pretraining by computing the mean of all time series in the pretrain set, then predicts that mean for all future time points.

Useful as:

• Simple baseline for global forecasting

• Test case for the pretraining API

• Educational examples

Parameters:

strategystr, one of {“mean”, “last”, “mean_by_index”}, default=”mean”

Strategy for prediction:

• "mean": predict mean of all values in pretrain set

• "last": predict last value from fit data

• "mean_by_index": predict mean computed per time index across pretraining series. Useful for cold start scenarios where pattern by index matters.

Attributes:

global_mean_float

Mean of all values in pretrain set (set after pretrain)

global_std_float

Standard deviation across pretrain data (set after pretrain)

n_pretrain_instances_int

Number of instances in pretrain data (set after pretrain)

n_pretrain_timepoints_int

Total number of time points in pretrain data (set after pretrain)

last_value_float or array-like

Last value from fit data (set after fit)

mean_by_index_pd.Series

Mean value at each time index across pretrain series (set after pretrain when strategy=”mean_by_index”)

Examples

>>> from sktime.forecasting.dummy_global import DummyGlobalForecaster
>>> from sktime.utils._testing.hierarchical import _make_hierarchical
>>> # Create panel of training data
>>> y_panel = _make_hierarchical(
...     hierarchy_levels=(2,), min_timepoints=10, max_timepoints=10
... )
>>> forecaster = DummyGlobalForecaster()
>>> forecaster.pretrain(y_panel)  # Learn global mean
DummyGlobalForecaster()
>>> # Now fit to a specific series
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> forecaster.fit(y)  # Set context
DummyGlobalForecaster()
>>> y_pred = forecaster.predict(fh=[1, 2, 3])  # Predict global mean
>>> y_pred.shape
(3,)

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.