TransformerPipeline#

class TransformerPipeline(steps)[source]#

Pipeline of transformers compositor.

The TransformerPipeline compositor allows to chain transformers. The pipeline is constructed with a list of sktime transformers, i.e. estimators following the BaseTransformer interface. The list can be unnamed (a simple list of transformers) or string named (a list of pairs of string, estimator).

For a list of transformers trafo1, trafo2, …, trafoN, the pipeline behaves as follows:

  • fit

    Changes state by running trafo1.fit_transform, trafo2.fit_transform` etc sequentially, with trafo[i] receiving the output of trafo[i-1]

  • transform

    Result is of executing trafo1.transform, trafo2.transform, etc with trafo[i].transform input = output of trafo[i-1].transform, and returning the output of trafoN.transform

  • inverse_transform

    Result is of executing trafo[i].inverse_transform, with trafo[i].inverse_transform input = output trafo[i-1].inverse_transform, and returning the output of trafoN.inverse_transform

  • update

    Changes state by chaining trafo1.update, trafo1.transform, trafo2.update, trafo2.transform, …, trafoN.update, where trafo[i].update and trafo[i].transform receive as input the output of trafo[i-1].transform

The get_params, set_params uses sklearn compatible nesting interface if list is unnamed, names are generated as names of classes if names are non-unique, f"_{str(i)}" is appended to each name string where i is the total count of occurrence of a non-unique string inside the list of names leading up to it (inclusive)

A TransformerPipeline can also be created by using the magic multiplication on any transformer, i.e., any estimator inheriting from BaseTransformer for instance, my_trafo1 * my_trafo2 * my_trafo3 will result in the same object as obtained from the constructor TransformerPipeline([my_trafo1, my_trafo2, my_trafo3]) A magic multiplication can also be used with (str, transformer) pairs, as long as one element in the chain is a transformer

Parameters:
stepslist of sktime transformers, or

list of tuples (str, transformer) of sktime transformers these are “blueprint” transformers, states do not change when fit is called

Attributes:
steps_list of tuples (str, transformer) of sktime transformers

clones of transformers in steps which are fitted in the pipeline is always in (str, transformer) format, even if steps is just a list strings not passed in steps are replaced by unique generated strings i-th transformer in steps_ is clone of i-th in steps

Examples

>>> from sktime.transformations.series.exponent import ExponentTransformer
>>> t1 = ExponentTransformer(power=2)
>>> t2 = ExponentTransformer(power=0.5)

Example 1, option A: construct without strings (unique names are generated for the two components t1 and t2)

>>> pipe = TransformerPipeline(steps = [t1, t2])

Example 1, option B: construct with strings to give custom names to steps

>>> pipe = TransformerPipeline(
...         steps = [
...             ("trafo1", t1),
...             ("trafo2", t2),
...         ]
...     )

Example 1, option C: for quick construction, the * dunder method can be used

>>> pipe = t1 * t2

Example 2: sklearn transformers can be used in the pipeline. If applied to Series, sklearn transformers are applied by series instance. If applied to Table, sklearn transformers are applied to the table as a whole.

>>> from sklearn.preprocessing import StandardScaler
>>> from sktime.transformations.series.summarize import SummaryTransformer

This applies the scaler per series, then summarizes:

>>> pipe = StandardScaler() * SummaryTransformer()

This applies the sumamrization, then scales the full summary table:

>>> pipe = SummaryTransformer() * StandardScaler()

This scales the series, then summarizes, then scales the full summary table:

>>> pipe = StandardScaler() * SummaryTransformer() * StandardScaler()

Methods

check_is_fitted()

Check if the estimator has been fitted.

clone()

Obtain a clone of the object with same hyper-parameters.

clone_tags(estimator[, tag_names])

Clone tags from another estimator as dynamic override.

create_test_instance([parameter_set])

Construct Estimator instance if possible.

create_test_instances_and_names([parameter_set])

Create list of all test instances and a list of names for them.

fit(X[, y])

Fit transformer to X, optionally to y.

fit_transform(X[, y])

Fit to data, then transform it.

get_class_tag(tag_name[, tag_value_default])

Get a class tag's value.

get_class_tags()

Get class tags from the class and all its 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()

Get object's parameter names.

get_params([deep])

Get parameters of estimator.

get_tag(tag_name[, tag_value_default, ...])

Get tag value from estimator class and dynamic tag overrides.

get_tags()

Get tags from estimator class and dynamic tag overrides.

get_test_params([parameter_set])

Return testing parameter settings for the estimator.

inverse_transform(X[, y])

Inverse transform X and return an inverse transformed version.

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.

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.

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 dynamic tags to given values.

transform(X[, y])

Transform X and return a transformed version.

update(X[, y, update_params])

Update transformer with X, optionally y.

classmethod get_test_params(parameter_set='default')[source]#

Return testing parameter settings for the estimator.

Parameters:
parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return "default" set.

Returns:
paramsdict or list of dict, default={}

Parameters to create testing instances of the class. Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params.

check_is_fitted()[source]#

Check if the estimator has been fitted.

Raises:
NotFittedError

If the estimator has not been fitted yet.

clone()[source]#

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.

Raises:
RuntimeError if the clone is non-conforming, due to faulty __init__.

Notes

If successful, equal in value to type(self)(**self.get_params(deep=False)).

clone_tags(estimator, tag_names=None)[source]#

Clone tags from another estimator as dynamic override.

Parameters:
estimatorestimator inheriting from :class:BaseEstimator
tag_namesstr or list of str, default = None

Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns:
Self

Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct Estimator instance if possible.

Parameters:
parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:
instanceinstance of the class with default parameters

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')[source]#

Create list of all test instances and a list of names for them.

Parameters:
parameter_setstr, default=”default”

Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:
objslist of instances of cls

i-th instance is cls(**cls.get_test_params()[i])

nameslist of str, same length as objs

i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}

fit(X, y=None)[source]#

Fit transformer to X, optionally to y.

State change:

Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. _X : X, coerced copy of X, if remember_data tag is True

possibly coerced to inner type or update_data compatible type by reference, when possible

model attributes (ending in “_”) : dependent on estimator

Parameters:
Xtime series in sktime compatible data container format

Data to fit transform to, of sktime type as follows: Series: interpreted as single time series

pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)

Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)

Hierarchical: pd.DataFrame with 3- or more-level MultiIndex

highest (rightmost) level of MultiIndex is time

for more details on sktime mtype format specifications, and additional valid type specifications, refer to

examples/AA_datatypes_and_datasets.ipynb

yoptional, time series in sktime compatible data format, default=None

Additional data, e.g., labels for transformation some transformers require this, see class docstring for details

Returns:
selfa fitted instance of the estimator
fit_transform(X, y=None)[source]#

Fit to data, then transform it.

Fits the transformer to X and y and returns a transformed version of X.

State change:

Changes state to “fitted”.

Writes to self: _is_fitted : flag is set to True. _X : X, coerced copy of X, if remember_data tag is True

possibly coerced to inner type or update_data compatible type by reference, when possible

model attributes (ending in “_”) : dependent on estimator

Parameters:
Xtime series in sktime compatible data container format

Data to transform, of sktime type as follows: Series: interpreted as single time series

pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)

Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)

Hierarchical: pd.DataFrame with 3- or more-level MultiIndex

highest (rightmost) level of MultiIndex is time

for more details on sktime mtype format specifications, and additional valid type specifications, refer to

examples/AA_datatypes_and_datasets.ipynb

yoptional, time series in sktime compatible data format, default=None

Additional data, e.g., labels for transformation some transformers require this, see class docstring for details

Returns:
transformed version of X
type depends on type of X and scitype:transform-output tag:
X | tf-output | type of return |

|----------|————–|------------------------| | Series | Primitives | pd.DataFrame (1-row) | | Panel | Primitives | pd.DataFrame | | Series | Series | Series | | Panel | Series | Panel | | Series | Panel | Panel |

instances in return correspond to instances in X
combinations not in the table are currently not supported
Explicitly, with examples:
if X is Series (e.g., pd.DataFrame) and transform-output is Series

then the return is a single Series of the same mtype Example: detrending a single series

if X is Panel (e.g., pd-multiindex) and transform-output is Series
then the return is Panel with same number of instances as X

(the transformer is applied to each input Series instance)

Example: all series in the panel are detrended individually

if X is Series or Panel and transform-output is Primitives

then the return is pd.DataFrame with as many rows as instances in X Example: i-th row of the return has mean and variance of the i-th series

if X is Series and transform-output is Panel

then the return is a Panel object of type pd-multiindex Example: i-th instance of the output is the i-th window running over X

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get a class tag’s value.

Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Parameters:
tag_namestr

Name of tag value.

tag_value_defaultany

Default/fallback value if tag is not found.

Returns:
tag_value

Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()[source]#

Get class tags from the class and all its parent classes.

Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Returns:
collected_tagsdict

Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.

get_config()[source]#

Get config flags for self.

Returns:
config_dictdict

Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

get_fitted_params(deep=True)[source]#

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:
deepbool, default=True

Whether to return fitted parameters of components.

  • If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).

  • If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:
fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

  • always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object

  • if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value

  • if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

classmethod get_param_defaults()[source]#

Get object’s parameter defaults.

Returns:
default_dict: dict[str, Any]

Keys are all parameters of cls that have a default defined in __init__ values are the defaults, as defined in __init__.

classmethod get_param_names()[source]#

Get object’s parameter names.

Returns:
param_names: list[str]

Alphabetically sorted list of parameter names of cls.

get_params(deep=True)[source]#

Get parameters of estimator.

Parameters:
deepboolean, optional

If True, will return the parameters for this estimator and contained sub-objects that are estimators.

Returns:
paramsmapping of string to any

Parameter names mapped to their values.

get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

Get tag value from estimator class and dynamic tag overrides.

Parameters:
tag_namestr

Name of tag to be retrieved

tag_value_defaultany type, optional; default=None

Default/fallback value if tag is not found

raise_errorbool

whether a ValueError is raised when the tag is not found

Returns:
tag_valueAny

Value of the tag_name tag in self. If not found, returns an error if raise_error is True, otherwise it returns tag_value_default.

Raises:
ValueError if raise_error is True i.e. if tag_name is not in
self.get_tags().keys()
get_tags()[source]#

Get tags from estimator class and dynamic tag overrides.

Returns:
collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

inverse_transform(X, y=None)[source]#

Inverse transform X and return an inverse transformed version.

Currently it is assumed that only transformers with tags

“scitype:transform-input”=”Series”, “scitype:transform-output”=”Series”,

have an inverse_transform.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : accessed by _inverse_transform

Parameters:
Xtime series in sktime compatible data container format

Data to inverse transform, of sktime type as follows: Series: interpreted as single time series

pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)

Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)

Hierarchical: pd.DataFrame with 3- or more-level MultiIndex

highest (rightmost) level of MultiIndex is time

for more details on sktime mtype format specifications, and additional valid type specifications, refer to

examples/AA_datatypes_and_datasets.ipynb

yoptional, time series in sktime compatible data format, default=None

Additional data, e.g., labels for transformation some transformers require this, see class docstring for details

Returns:
inverse transformed version of X

of the same type as X, and conforming to mtype format specifications

is_composite()[source]#

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:
composite: bool, whether self contains a parameter which is BaseObject
property is_fitted[source]#

Whether fit has been called.

classmethod load_from_path(serial)[source]#

Load object from file location.

Parameters:
serialresult of ZipFile(path).open(“object)
Returns:
deserialized self resulting in output at path, of cls.save(path)
classmethod load_from_serial(serial)[source]#

Load object from serialized memory container.

Parameters:
serial1st element of output of cls.save(None)
Returns:
deserialized self resulting in output serial, of cls.save(None)
reset()[source]#

Reset the object to a clean post-init state.

Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:

  • hyper-parameters = arguments of __init__

  • object attributes containing double-underscores, i.e., the string “__”

Class and object methods, and class attributes are also unaffected.

Returns:
self

Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

Notes

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

save(path=None, serialization_format='pickle')[source]#

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:
pathNone or file location (str or Path)

if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

serialization_format: str, default = “pickle”

Module to use for serialization. The available options are “pickle” and “cloudpickle”. Note that non-default formats might require installation of other soft dependencies.

Returns:
if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file
set_config(**config_dict)[source]#

Set config flags to given values.

Parameters:
config_dictdict

Dictionary of config name : config value pairs. Valid configs, values, and their meaning is listed below:

displaystr, “diagram” (default), or “text”

how jupyter kernels display instances of self

  • “diagram” = html box diagram representation

  • “text” = string printout

print_changed_onlybool, default=True

whether printing of self lists only self-parameters that differ from defaults (False), or all parameter names and values (False). Does not nest, i.e., only affects self and not component estimators.

warningsstr, “on” (default), or “off”

whether to raise warnings, affects warnings from sktime only

  • “on” = will raise warnings from sktime

  • “off” = will not raise warnings from sktime

backend:parallelstr, optional, default=”None”

backend to use for parallelization when broadcasting/vectorizing, one of

  • “None”: executes loop sequentally, simple list comprehension

  • “loky”, “multiprocessing” and “threading”: uses joblib.Parallel

  • “joblib”: custom and 3rd party joblib backends, e.g., spark

  • “dask”: uses dask, requires dask package in environment

backend:parallel:paramsdict, optional, default={} (no parameters passed)

additional parameters passed to the parallelization backend as config. Valid keys depend on the value of backend:parallel:

  • “None”: no additional parameters, backend_params is ignored

  • “loky”, “multiprocessing” and “threading”: default joblib backends any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.

  • “joblib”: custom and 3rd party joblib backends, e.g., spark. Any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, backend must be passed as a key of backend_params in this case. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.

  • “dask”: any valid keys for dask.compute can be passed, e.g., scheduler

input_conversionstr, one of “on” (default), “off”, or valid mtype string

controls input checks and conversions, for _fit, _transform, _inverse_transform, _update

  • "on" - input check and conversion is carried out

  • "off" - input check and conversion are not carried out before passing data to inner methods

  • valid mtype string - input is assumed to specified mtype, conversion is carried out but no check

output_conversionstr, one of “on”, “off”, valid mtype string

controls output conversion for _transform, _inverse_transform

  • "on" - if input_conversion is “on”, output conversion is carried out

  • "off" - output of _transform, _inverse_transform is directly returned

  • valid mtype string - output is converted to specified mtype

Returns:
selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

set_params(**kwargs)[source]#

Set the parameters of estimator.

Valid parameter keys can be listed with get_params().

Returns:
selfreturns an instance of self.
set_random_state(random_state=None, deep=True, self_policy='copy')[source]#

Set random_state pseudo-random seed parameters for self.

Finds random_state named parameters via estimator.get_params, and sets them to integers derived from random_state via set_params. These integers are sampled from chain hashing via sample_dependent_seed, and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in estimator depending on self_policy, and remaining component estimators if and only if deep=True.

Note: calls set_params even if self does not have a random_state, or none of the components have a random_state parameter. Therefore, set_random_state will reset any scikit-base estimator, even those without a random_state parameter.

Parameters:
random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.

deepbool, default=True

Whether to set the random state in sub-estimators. If False, will set only self’s random_state parameter, if exists. If True, will set random_state parameters in sub-estimators as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”
  • “copy” : estimator.random_state is set to input random_state

  • “keep” : estimator.random_state is kept as is

  • “new” : estimator.random_state is set to a new random state,

derived from input random_state, and in general different from it

Returns:
selfreference to self
set_tags(**tag_dict)[source]#

Set dynamic tags to given values.

Parameters:
**tag_dictdict

Dictionary of tag name: tag value pairs.

Returns:
Self

Reference to self.

Notes

Changes object state by setting tag values in tag_dict as dynamic tags in self.

transform(X, y=None)[source]#

Transform X and return a transformed version.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : optionally accessed, only available if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _transform

Parameters:
Xtime series in sktime compatible data container format

Data to fit transform to, of sktime type as follows: Series: interpreted as single time series

pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)

Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)

Hierarchical: pd.DataFrame with 3- or more-level MultiIndex

highest (rightmost) level of MultiIndex is time

for more details on sktime mtype format specifications, and additional valid type specifications, refer to

examples/AA_datatypes_and_datasets.ipynb

yoptional, time series in sktime compatible data format, default=None

Additional data, e.g., labels for transformation some transformers require this, see class docstring for details

Returns:
transformed version of X
type depends on type of X and scitype:transform-output tag:

transform

X

-output

type of return

Series

Primitives

pd.DataFrame (1-row)

Panel

Primitives

pd.DataFrame

Series

Series

Series

Panel

Series

Panel

Series

Panel

Panel

instances in return correspond to instances in X
combinations not in the table are currently not supported
Explicitly, with examples:
if X is Series (e.g., pd.DataFrame) and transform-output is Series

then the return is a single Series of the same mtype Example: detrending a single series

if X is Panel (e.g., pd-multiindex) and transform-output is Series
then the return is Panel with same number of instances as X

(the transformer is applied to each input Series instance)

Example: all series in the panel are detrended individually

if X is Series or Panel and transform-output is Primitives

then the return is pd.DataFrame with as many rows as instances in X Example: i-th row of the return has mean and variance of the i-th series

if X is Series and transform-output is Panel

then the return is a Panel object of type pd-multiindex Example: i-th instance of the output is the i-th window running over X

update(X, y=None, update_params=True)[source]#

Update transformer with X, optionally y.

State required:

Requires state to be “fitted”.

Accesses in self: _is_fitted : must be True _X : accessed by _update and by update_data, if remember_data tag is True fitted model attributes (ending in “_”) : must be set, accessed by _update

Writes to self: _X : updated by values in X, via update_data, if remember_data tag is True fitted model attributes (ending in “_”) : only if update_params=True

type and nature of update are dependent on estimator

Parameters:
Xtime series in sktime compatible data container format

Data to update transform with, of sktime type as follows: Series: interpreted as single time series

pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) if np.ndarray, of shape (n_timepoints) or (n_variables, n_timepoints)

Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

pd.DataFrame in long/wide format, or 3D np.ndarray if pd.DataFrame with 2-level MultiIndex, index is (instance, time) if 3D np.ndarray, of shape (n_instances, n_variables, n_timepoints)

Hierarchical: pd.DataFrame with 3- or more-level MultiIndex

highest (rightmost) level of MultiIndex is time

for more details on sktime mtype format specifications, and additional valid type specifications, refer to

examples/AA_datatypes_and_datasets.ipynb

yoptional, time series in sktime compatible data format, default=None

Additional data, e.g., labels for transformation some transformers require this, see class docstring for details

Returns:
selfa fitted instance of the estimator