EnbPIForecaster

EnbPIForecaster

class EnbPIForecaster(forecaster=None, bootstrap_transformer=None, random_state=None, aggregation_function='mean')[source]

Ensemble Bootstrap Prediction Interval Forecaster.

The forecaster combines sktime forecasters, with tsbootstrap bootstrappers and the EnbPI algorithm [1] implemented in fortuna using the tutorial from this blogpost [2].

The forecaster is similar to the the bagging forecaster and performs internally the following steps.

For training:

1. Uses a bootstrap transformer to generate bootstrap samples

   and returning the corresponding indices of the original time series. Note that the bootstrap transformer must be able to return indices of the original time series as an additional column. I.e., the bootstrap_transformer must have the capability:bootstrap_indices tag, and its parameter return_indices must be set to True.

2. Fit a forecaster on the first n - max(fh) values of each

   bootstrap sample

3. Uses each forecaster to predict the last max(fh) values of each

   bootstrap sample

For Prediction:

1. Average the predictions of each fitted forecaster using the

   aggregation function

For Probabilistic Forecasting:

1. Calculate the point forecast by average the prediction of each

   fitted forecaster using the aggregation function

2. Passes the indices of the bootstrapped samples, the predictions

   from the fit call, the point prediction of the test set, and the desired error rate to the EnbPI algorithm to calculate the prediction intervals. For more information on the EnbPI algorithm, see the references and the documentation of the EnbPI class in aws-fortuna.

Parameters:

forecasterestimator

The base forecaster to fit to each bootstrap sample.

bootstrap_transformertsbootstrap.BootstrapTransformer

The transformer to fit to the target series to generate bootstrap samples. This transformer must be able to return the indices of the original time series as an additional column. I.e., the bootstrap_transformer must have the capability:bootstrap_indices tag, and its parameter return_indices must be set to True.

random_stateint, RandomState instance or None, default=None

Random state for reproducibility.

aggregation_functionstr, default=”mean”

The aggregation function to use for combining the predictions of the fitted forecasters. Either “mean” or “median”.

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.

References

[1]

Chen Xu & Yao Xie (2021). Conformal Prediction Interval for Dynamic

Time-Series. .. [R09383241d85a-2] Valeriy Manokhin, PhD, MBA, CQF. Demystifying EnbPI: Mastering Conformal Prediction Forecasting

Examples

>>> import numpy as np
>>> from tsbootstrap import MovingBlockBootstrap
>>> from sktime.forecasting.enbpi import EnbPIForecaster
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.datasets import load_airline
>>> from sktime.transformations.difference import Differencer
>>> from sktime.transformations.detrend import Deseasonalizer
>>> from sktime.forecasting.base import ForecastingHorizon
>>> y = load_airline()
>>> forecaster = Differencer(lags=[1]) * Deseasonalizer(sp=12) * EnbPIForecaster(
...    forecaster=NaiveForecaster(sp=12),
...    bootstrap_transformer=MovingBlockBootstrap(n_bootstraps=10))
>>> fh = ForecastingHorizon(np.arange(1, 13))
>>> forecaster.fit(y, fh=fh)
TransformedTargetForecaster(...)
>>> res = forecaster.predict()
>>> res_int = forecaster.predict_interval(coverage=[0.5])

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()	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.