BoxCoxBiasAdjustedForecaster

BoxCoxBiasAdjustedForecaster

class BoxCoxBiasAdjustedForecaster(forecaster, lambda_fixed=None)[source]

Box-Cox Bias-Adjusted Forecaster.

This module implements a forecaster that applies Box-Cox transformation and bias adjustment to the predictions of a wrapped forecaster.

The bias adjustment is applied to both point predictions and prediction intervals using the bias correction formula for inverse Box-Cox transforms:

\[y_t = \text{inv\_boxcox}(w_t, \lambda) \cdot [1 + \sigma_t^2(1-\lambda)/ (2(\lambda w_t + 1)^2)]\]

where: - \(y_t\) is the back-transformed forecast at time t - \(w_t\) is the transformed data at time t - \(\lambda\) is the Box-Cox parameter - \(\sigma_t^2\) is the forecast variance on the transformed scale at time t

Parameters:

forecasterBaseForecaster

The wrapped forecaster to which Box-Cox transformation and bias adjustment will be applied.

lambda_fixedfloat, optional (default=None)

The Box-Cox transformation parameter. If None, it will be estimated.

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.

Notes

This forecaster applies only to univariate, non-hierarchical inner forecasters.

References

[1]

Hyndman, R.J., & Athanasopoulos, G. (2018) “Forecasting: principles and practice”, 2nd edition, Section 2.7 - Box-Cox Transformations, OTexts: Melbourne, Australia. OTexts.com/fpp2

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.