ColumnEnsembleForecaster

Forecast each series with separate forecaster.

Quickstart

from sktime.forecasting.compose import ColumnEnsembleForecaster

estimator = ColumnEnsembleForecaster(forecasters)

Parameters(1)

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

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 ()