Transformer
OptimalReconciler
Inverse transformUnequal-length series
Reconciliation for hierarchical time series.
Uses all the forecasts to obtain a reconciled forecast. Uses the constraint matrix approach, which is more efficient than the projection one.
If the dataframe is not hierarchical, this works as identity.
Quickstart
python
from sktime.transformations.hierarchical.reconcile import OptimalReconciler
estimator = OptimalReconciler(error_covariance_matrix: DataFrame=None, alpha=0)Tags
Capabilities
- Inverse transform: Supported
- Unequal-length series
- Multivariate: Not supported
- Missing values: Not supported
- Removes missing values: Not supported
- Equalizes series length: Not supported
Properties
- Input typescitype:transform-input
- Series
- Output typescitype:transform-output
- Series
- Label typescitype:transform-labels
- None
- Fit is emptyfit_is_empty
- No
- Keeps the time indextransform-returns-same-time-index
- No
- Requires Xrequires_X
- Yes
- Requires yrequires_y
- No
- X and y need the same indexX-y-must-have-same-index
- No
Parameters(2)
- error_covariance_matrixpd.DataFrame, default=None
- Error covariance matrix. If None, it is assumed to be the identity matrix.
- alphafloat, default=0
- Constant added to the diagonal of the inverted matrix.
Examples
>>> from sktime.transformations.hierarchical.reconcile import (
... OptimalReconciler)
>>> from sktime.utils._testing.hierarchical import _make_hierarchical
>>> from sktime.forecasting.naive import NaiveForecaster
>>> y = _make_hierarchical ()
>>> pipe = OptimalReconciler () * NaiveForecaster ()
>>> pipe = pipe. fit (y)
>>> y_pred = pipe. predict (fh = [1, 2, 3 ])