ColumnEnsembleForecaster

class ColumnEnsembleForecaster(forecasters)[source]

Forecast each series with separate forecaster.

Applies different forecasters by columns.

ColumnEnsembleForecaster is passed forecaster/index pairs, exact syntax below. Index can be single pandas index element, pd.Index, int, str, or list thereof. If iterable (pd.Index, list), refers to multiple columns.

Behaviour in fit, predict, update: For index pairs f_i, ix_i passed, applies forecaster f_i to column(s) ix_i. predict results are concatenated to one container with same columns as in fit.

Parameters:

forecasterssktime forecaster, or list of tuples (str, estimator, int or pd.index)

if tuples, with name = str, estimator is forecaster, index as int or index
if last element is index, it must be int, str, or pd.Index coercible
if last element is int x, and is not in columns, is interpreted as x-th column

All columns must be present in an index

If forecaster, clones of forecaster are applied to all columns.
If list of tuples, forecaster in tuple is applied to column with int/str index

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.

Examples

>>> import pandas as pd
>>> from sktime.forecasting.compose import ColumnEnsembleForecaster
>>> from sktime.forecasting.naive import NaiveForecaster
>>> from sktime.forecasting.trend import PolynomialTrendForecaster
>>> from sktime.datasets import load_longley

Using integers (column iloc references) for indexing:

>>> y = load_longley()[1][["GNP", "UNEMP"]]
>>> forecasters = [
...     ("trend", PolynomialTrendForecaster(), 0),
...     ("naive", NaiveForecaster(), 1),
... ]
>>> forecaster = ColumnEnsembleForecaster(forecasters=forecasters)
>>> forecaster.fit(y, fh=[1, 2, 3])
ColumnEnsembleForecaster(...)
>>> y_pred = forecaster.predict()

Using strings for indexing:

>>> df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
>>> fc = ColumnEnsembleForecaster(
...     [("foo", NaiveForecaster(), "a"), ("bar", NaiveForecaster(), "b")]
... )
>>> fc.fit(df, fh=[1, 42])
ColumnEnsembleForecaster(...)
>>> y_pred = fc.predict()

Applying one forecaster to multiple columns, multivariate:

>>> df = pd.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
>>> fc = ColumnEnsembleForecaster(
...    [("ab", NaiveForecaster(), ["a", 1]), ("c", NaiveForecaster(), 2)]
... )
>>> fc.fit(df, fh=[1, 42])
ColumnEnsembleForecaster(...)
>>> y_pred = fc.predict()

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 parameters of estimator.
`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 composite.
`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`(**kwargs)	Set the parameters of estimator.
`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.