DirectReductionForecaster

DirectReductionForecaster

class DirectReductionForecaster(estimator, window_length=10, transformers=None, X_treatment='concurrent', impute_method='bfill', pooling='local', windows_identical=False)[source]

Direct reduction forecaster, incl single-output, multi-output, exogenous Dir.

Implements direct reduction, of forecasting to tabular regression.

For no X, defaults to DirMO (direct multioutput) for X_treatment = "concurrent", and simple direct (direct single-output) for X_treatment = "shifted".

Direct single-output with concurrent X behaviour can be configured by passing a single-output scikit-learn compatible transformer.

Algorithm details:

In fit, given endogeneous time series y and possibly exogenous X: fits estimator to feature-label pairs as defined as follows.

• if X_treatment = "concurrent": features = y(t), y(t-1), …, y(t-window_size), if provided X(t+h); labels = y(t+h) for h in the forecasting horizon, ranging over all t where the above have been observed (are in the index) for each h in the forecasting horizon (separate estimator fitted per h)

• if X_treatment = "shifted": features = y(t), y(t-1), …, y(t-window_size), if provided: X(t); labels = y(t+h_1), …, y(t+h_k) for h_j in the forecasting horizon ranging over all t where the above have been observed (are in the index) estimator is fitted as a multi-output estimator (for all h_j simultaneously)

In predict, given possibly exogenous X, at cutoff time c,

• if X_treatment = "concurrent": applies fitted estimators’ predict to feature = y(c), y(c-1), …, y(c-window_size), if provided X(c+h); to obtain a prediction for y(c+h), for each h in the forecasting horizon

• if X_treatment = "shifted": applies fitted estimator’s predict to features = y(c), y(c-1), …, y(c-window_size), if provided: X(c); to obtain prediction for y(c+h_1), …, y(c+h_k) for h_j in forecasting horizon

Parameters:

estimatorsklearn regressor, must be compatible with sklearn interface

tabular regression algorithm used in reduction algorithm

window_lengthint, optional, default=10

window length used in the reduction algorithm

transformerscurrently not used

X_treatmentstr, optional, one of “concurrent” (default) or “shifted”

determines the timestamps of X from which y(t+h) is predicted, for horizon h

• “concurrent”: y(t+h) is predicted from lagged y, and X(t+h), for all h in fh in particular, if no y-lags are specified, y(t+h) is predicted from X(t)

• “shifted”: y(t+h) is predicted from lagged y, and X(t), for all h in fh in particular, if no y-lags are specified, y(t+h) is predicted from X(t+h)

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, see there. To pass further parameters, pass the Imputer transformer directly, as described below.

• if sktime transformer, this transformer is applied to the lagged data. This needs to be a transformer that removes missing data, and can be an Imputer.

• if None, no imputation is done when applying Lag transformer

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 ignoring levels

• “panel” = second lowest level, one reduced model per panel level (-2)

if there are 2 or less levels, “global” and “panel” result in the same if there is only 1 level (single time series), all three settings agree

windows_identicalbool, optional, default=False

Specifies whether all direct models use the same number of observations or a different number of observations.

• True : Uniform window of length (total observations - maximum forecasting horizon). Note: Currently, there are no missing arising from window length due to backwards imputation in ReductionTransformer. Without imputation, the window size corresponds to (total observations + 1 - window_length + maximum forecasting horizon).

• False : Window size differs for each forecasting horizon. Window length corresponds to (total observations + 1 - window_length + forecasting horizon).

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.

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.