MSTL#

Season-Trend decomposition using LOESS for multiple seasonalities.

Direct interface for statsmodels.tsa.seasonal.MSTL for transform, with sktime native extensions to allow use in forecasting pipelines.

MSTL can be used to perform deseasonalization or decomposition:

fit stores the decomposed values in self.trend_, self.seasonal_, and self.resid_.

If return_components=False, then transform returns a pd.Series of the deseasonalized values, i.e., trend plus residual component. The individual seasonal and residual components can be found in self.trend_ and self.resid_.

If return_components=True, then transform returns a full components decomposition, in a DataFrame with cols (for each input column), in this order:

“trend” - the trend component
“resid” - the residuals after de-trending, de-seasonalizing
“seasonal” - a single sum-of-seasonalities component, if periods is None.
“seasonal_<period>” - the seasonal component(s), where <period> is an integer indicating the periodicity, one such component per element in periods, if periods is an array-like of integers.

MSTL performs inverse_transform by reconstituting the signal from its components, and can be used for pipelining in a TransformedTargetForecaster, see examples below.

if periods are provided, the transformation will deseasonalize, and reseasonalize after forecast.
if periods are not provided, and return_components=False, the forecast will be a pure trend forecast, using sum of trend and residual components.
if return_components=True, the forecaster has access to all components, and can apply different forecasters to different components.

See the examples below for usage.

For automated detection of seasonalities using a custom seasonality detection algorithm, pipeline MSTL with the respective estimator, e.g., SeasonalityACF.

Parameters:

endogarray_like

Data to be decomposed. Must be squeezable to 1-d.

periods{int, array_like, None}, optional

Periodicity of the seasonal components. If None and endog is a pandas Series or DataFrame, attempts to determine from endog. If endog is a ndarray, periods must be provided.

windows{int, array_like, None}, optional

Length of the seasonal smoothers for each corresponding period. Must be an odd integer, and should normally be >= 7 (default). If None then default values determined using 7 + 4 * np.arange(1, n + 1, 1) where n is number of seasonal components.

lmbda{float, str, None}, optional

The lambda parameter for the Box-Cox transform to be applied to endog prior to decomposition. If None, no transform is applied. If auto, a value will be estimated that maximizes the log-likelihood function.

iterateint, optional

Number of iterations to use to refine the seasonal component.

stl_kwargsdict, optional

Arguments to pass to STL.

return_componentsbool, default=False

if False, will return only the MSTL transformed series, same as trend plus residual component. The resulting series has the same number of columns as the input.
if True, will return all components of the decomposition,
a multivariate series with DataFrame cols (for each input column):
- “trend” - the trend component
- “resid” - the residuals after de-trending, de-seasonalizing
- “seasonal” - a single sum-of-seasonalities component, if
periods is None. * “seasonal_<period>” - the seasonal component(s),

where <period> is an integer indicating the periodicity, one such component per element in periods

All components together sum up to the original series, in-sample.

Attributes:

trend_pd.Series: Trend component of series seen in fit.
resid_pd.Series: Residuals component of series seen in fit.
seasonal_pd.DataFrame: If periods is None, this contains a single column, with the sum of all seasonal components of the X seen in fit. If periods is an array-like of integers, this consists of multiple columns seasonal_<period>, each corresponding to a seasonal component of the series.

References

[1] https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.MSTL.html

Examples

Simple use case: decompose a time series into trend, seasonal, residual components >>> import matplotlib.pyplot as plt # doctest: +SKIP >>> from sktime.datasets import load_airline >>> from sktime.transformations.series.detrend import MSTL >>> y = load_airline() >>> y.index = y.index.to_timestamp() >>> mstl = MSTL(return_components=True) >>> mstl.fit(y) >>> res = mstl.transform(y) >>> res.plot() # doctest: +SKIP >>> plt.tight_layout() # doctest: +SKIP >>> plt.show() # doctest: +SKIP

MSTL can be pipelined with a forecaster for multiple deseasonalized forecasts. The following example uses a simple trend forecaster, applied to a series deseasonalized with MSTL at periods 2 and 12. After the trend forecast, the seasonal components are added back to the forecast automatically. >>> from sktime.datasets import load_airline >>> from sktime.transformations.series.detrend import MSTL >>> from sktime.forecasting.trend import TrendForecaster >>> >>> mstl_trafo = MSTL(periods=[2, 12]) >>> mstl_deseason_fcst = mstl_trafo * TrendForecaster() >>> y = load_airline() >>> mstl_deseason_fcst.fit(y, fh=[1, 2, 3]) >>> y_pred = mstl_deseason_fcst.predict()

MSTL can also be used to make forecasts using the full component decomposition. For this, set return_components=True when pipelining. The forecaster in the pipeline will then be given a multivariate series with the components as columns, i.e., “trend”, “resid”, “seasonal_2”, “seasonal_12”. To apply different forecasters to different components, use a ColumnEnsembleForecaster; to apply the same forecaster to all components, simply pipeline with the forecaster. The following example uses a TrendForecaster for the trend, a seasonal naive forecaster for the seasonal components, with different seasonalities, and a naive forecaster for the residuals. >>> from sktime.datasets import load_airline >>> from sktime.transformations.series.detrend import MSTL >>> from sktime.forecasting.compose import ColumnEnsembleForecaster >>> from sktime.forecasting.naive import NaiveForecaster >>> from sktime.forecasting.trend import TrendForecaster >>> >>> mstl_trafo_comp = MSTL(periods=[2, 12], return_components=True) >>> mstl_component_fcst = mstl_trafo_comp * ColumnEnsembleForecaster( … [ … (“trend”, TrendForecaster(), “trend”), … (“sp2”, NaiveForecaster(strategy=”last”, sp=2), “seasonal_2”), … (“sp12”, NaiveForecaster(strategy=”last”, sp=12), “seasonal_12”), … (“residual”, NaiveForecaster(strategy=”last”), “resid”), … ] … ) >>> y = load_airline() >>> mstl_component_fcst.fit(y, fh=[1, 2, 3]) >>> y_pred = mstl_component_fcst.predict()

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`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`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 composed of other BaseObjects.
`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`(**params)	Set the parameters of this object.
`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. There are currently no reserved values for forecasters.

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:

Sets fitted model attributes ending in “_”, fitted attributes are inspectable via get_fitted_params.

Sets self.is_fitted flag to True.

if self.get_tag("remember_data") is True, memorizes X as self._X, coerced to self.get_tag("X_inner_mtype").

Parameters:

Xtime series in sktime compatible data container format

Data to fit transform to.

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection of time series. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see transformer tutorial examples/03_transformers.ipynb

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

Additional data, e.g., labels for transformation If self.get_tag("requires_y") is True, must be passed in fit, not optional. For required format, 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 fit transform to, and data to transform.

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection of time series. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see transformer tutorial examples/03_transformers.ipynb

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

Additional data, e.g., labels for transformation If self.get_tag("requires_y") is True, must be passed in fit, not optional. For required format, 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(sort=True)[source]#

Get object’s parameter names.

Parameters:

sortbool, default=True: Whether to return the parameter names sorted in alphabetical order (True), or in the order they appear in the class __init__ (False).

Returns:

param_names: list[str]: List of parameter names of cls. If sort=False, in same order as they appear in the class __init__. If sort=True, alphabetically ordered.

get_params(deep=True)[source]#

Get a dict of parameters values for this object.

Parameters:

deepbool, default=True

Whether to return parameters of components.

If True, will return a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns:

paramsdict with str-valued keys

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

always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction
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

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:

Fitted model attributes ending in “_”.

self.is_fitted, must be True

Parameters:

Xtime series in sktime compatible data container format

Data to fit transform to.

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection of time series. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see transformer tutorial examples/03_transformers.ipynb

yoptional, data 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 composed of other BaseObjects.

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 an object has any parameters whose values are BaseObjects.

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(**params)[source]#

Set the parameters of this object.

The method works on simple estimators as well as on composite objects. Parameter key strings <component>__<parameter> can be used for composites, i.e., objects that contain other objects, to access <parameter> in the component <component>. The string <parameter>, without <component>__, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name <parameter>.

Parameters:

**paramsdict: BaseObject parameters, keys must be <component>__<parameter> strings. __ suffixes can alias full strings, if unique among get_params keys.

Returns:

selfreference to self (after parameters have been set)

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:

Fitted model attributes ending in “_”.

self.is_fitted, must be True

Parameters:

Xtime series in sktime compatible data container format

Data to transform.

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection of time series. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see transformer tutorial examples/03_transformers.ipynb

yoptional, data 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:

Fitted model attributes ending in “_”.

self.is_fitted, must be True

Writes to self:

Fitted model attributes ending in “_”.

if remember_data tag is True, writes to self._X, updated by values in X, via update_data.

Parameters:

Xtime series in sktime compatible data container format

Data to update transformation with

Individual data formats in sktime are so-called mtype specifications, each mtype implements an abstract scitype.

Series scitype = individual time series. pd.DataFrame, pd.Series, or np.ndarray (1D or 2D)
Panel scitype = collection of time series. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection of time series. pd.DataFrame with 3 or more level row MultiIndex (hierarchy_1, ..., hierarchy_n, time)

For further details on data format, see glossary on mtype. For usage, see transformer tutorial examples/03_transformers.ipynb

yoptional, data 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