Object and estimator tags#

Every first-class object in sktime is tagged with a set of tags that describe its properties and capabilities, or control its behavior.

Tags are key-value pairs, where the key is a string with the name of the tag. The value of the tag can have arbitrary type, and describes a property, capability, or controls behaviour of the object, depending on the value.

For instance, a forecaster may have the tag capability:pred_int: True if it can make probabilistic predictions. Users can find all forecasters that can make probabilistic predictions by filtering for this tag.

This API reference lists all tags available in sktime, and key utilities for their usage.

Inspecting tags, retrieving by tags#

to get the tags of an object, use the get_tags method. An object’s tags can depend on its hyper-parameters.
to get the tags of a class, use the get_tags method of the class. A class’s tags are static and do not depend on its hyper-parameters. By default, class tags that may vary for instances take the most “capable” value, in the case of capabilities.
to programmatically retrieve all tags available in sktime or for a particular type of object, at runtime, use the registry.all_tags utility
to programmatically retrieve all objects or estimators in sktime, filtered for values of tags, use the registry.all_estimators utility

`all_tags`([estimator_types, as_dataframe])	List all tags in sktime, for objects of a certain type.
`all_estimators`([estimator_types, ...])	List all estimators or objects in sktime, by scitype or tag.

General tags, packaging#

This section lists tags that are general and apply to all objects in sktime. These tags are typically used for typing, packaging and documentation purposes.

`object_type`	Scientific type of the object.
`maintainers`	Current maintainers of the object, GitHub IDs.
`authors`	Authors of the object, GitHub IDs.
`python_version`	Python version requirement specifier for the object (PEP 440).
`python_dependencies`	Python package dependency requirement specifiers for the object (PEP 440).
`python_dependencies_alias`	Alias for Python package dependency names for the object.
`env_marker`	Environment marker requirement for the object (PEP 508).
`requires_cython`	Whether the object requires a C compiler present, such as libomp, gcc.

Forecaster tags#

This section lists tags applying to forecasters. These tags are used to describe capabilities, properties, and behavior of forecasters.

The list also includes some developer facing tags that are used to control internal behavior of the forecaster.

`capability__exogeneous`	Capability: the forecaster can use exogeneous data.
`capability__insample`	Capability: the forecaster can make in-sample predictions.
`capability__pred_int`	Capability: the forecaster can make probabilistic or interval forecasts.
`capability__pred_int__insample`	Capability: the forecaster can make in-sample probabilistic forecasts.
`capability__missing_values`	Capability: the estimator can handle missing data, e.g,, NaNs.

Tags for classifiers, regressors, clustering#

`capability__multivariate`	Capability: the estimator can handle multivariate time series.
`capability__missing_values`	Capability: the estimator can handle missing data, e.g,, NaNs.
`capability__unequal_length`	Capability: the estimator can handle unequal length time series.
`capability__feature_importance`	Capability: the estimator can provide feature importance.
`capability__contractable`	Capability: the estimator can be asked to satisfy a maximum time contract.
`capability__train_estimate`	Capability: the algorithm can estimate its performance on the training set.