MCRecursiveProbaReductionForecaster

MCRecursiveProbaReductionForecaster

class MCRecursiveProbaReductionForecaster(estimator, window_length=10, n_samples=100, impute_method='bfill', pooling='local', random_state=None)[source]

Monte Carlo Recursive reduction forecaster with probabilistic prediction.

Implements recursive reduction with ancestral sampling for multi-step ahead probabilistic forecasting. Uses Monte Carlo sampling to generate multiple forecast trajectories, where each step is sampled from the predicted distribution conditioned on previously sampled values.

This approach is inspired by DeepAR’s ancestral sampling strategy, adapted for use with any tabular probabilistic regressor from skpro.

Algorithm details:

In fit, given endogenous time series y and possibly exogenous X:

fits estimator to feature-label pairs for one-step-ahead prediction: features = y(t), y(t-1), …, y(t-window_length+1), if provided: X(t+1); labels = y(t+1), ranging over all t where the above have been observed

In predict_proba, given possibly exogenous X, at cutoff time c:

1. Generate n_samples Monte Carlo trajectories using ancestral sampling

2. For each trajectory and each horizon step h:

   1. Get probabilistic prediction from estimator using lagged features

   2. Sample one value from the predicted distribution

   3. Use this sampled value as input for the next step

3. Construct empirical distribution from the n_samples trajectories

In predict, returns the mean of the empirical distribution from MC samples.

Parameters:

estimatorskpro probabilistic regressor

Tabular probabilistic regression algorithm used in reduction. Must support predict_proba method returning a distribution object.

window_lengthint, optional, default=10

Window length used in the reduction algorithm (number of lags).

n_samplesint, optional, default=100

Number of Monte Carlo sample trajectories to generate. Higher values give more accurate distribution estimates but slower inference.

impute_methodstr, None, or sktime transformation, optional

Imputation method to use for missing values in the lagged data. default=”bfill” if str, admissible strings are of Imputer.method parameter. if sktime transformer, this transformer is applied to the lagged data. if None, no imputation is done.

poolingstr, one of [“local”, “global”, “panel”], optional, default=”local”

Level on which data are pooled to fit the supervised regression model. “local” = unit/instance level, one reduced model per lowest hierarchy level “global” = top level, one reduced model overall, on pooled data “panel” = second lowest level, one reduced model per panel level (-2)

random_stateint, RandomState instance or None, optional, default=None

Controls the randomness of the Monte Carlo sampling.

Attributes:

estimator_fitted estimator

The fitted probabilistic regressor.

Examples

>>> from sklearn.linear_model import LinearRegression
>>> from skpro.regression.residual import ResidualDouble
>>> from sktime.datasets import load_airline
>>> from sktime.forecasting.compose import MCRecursiveProbaReductionForecaster
>>>
>>> y = load_airline()
>>>
>>> base_estimator = LinearRegression()
>>> estimator = ResidualDouble(base_estimator)
>>> forecaster = MCRecursiveProbaReductionForecaster(estimator)
>>>
>>> forecaster.fit(y)
MCRecursiveProbaReductionForecaster(...)
>>> y_pred_dist = forecaster.predict_proba(fh=range(1, 13))
>>> len(y_pred_dist.mean()) == 12
True

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.