STLBootstrapTransformer#

class STLBootstrapTransformer(n_series: int = 10, sp: int = 12, block_length: Optional[int] = None, sampling_replacement: bool = False, return_actual: bool = True, lambda_bounds: Optional[Tuple] = None, lambda_method: str = 'guerrero', seasonal: int = 7, trend: Optional[int] = None, low_pass: Optional[int] = None, seasonal_deg: int = 1, trend_deg: int = 1, low_pass_deg: int = 1, robust: bool = False, seasonal_jump: int = 1, trend_jump: int = 1, low_pass_jump: int = 1, inner_iter: Optional[int] = None, outer_iter: Optional[int] = None, random_state: Optional[Union[int, numpy.random.mtrand.RandomState]] = None)[source]#

Creates a population of similar time series.

This method utilises a form of bootstrapping to generate a population of similar time series to the input time series [1], [2].

First the observed time series is transformed using a Box-Cox transformation to stabilise the variance. Then it’s decomposed to seasonal, trend and residual time series, using the STL implementation from statsmodels (statsmodels.tsa.api.STL) [4]. We then sample blocks from the residuals time series using the Moving Block Bootstrapping (MBB) method [3] to create synthetic residuals series that mimic the autocorrelation patterns of the observed series. Finally these bootstrapped residuals are added to the season and trend components and we use the inverse Box-Cox transform to return a panel of similar time series. The output can be used for bagging forecasts, prediction intervals and data augmentation.

The returned panel will be a multiindex dataframe (pd.DataFrame) with the series_id and time_index as the index and a single column of the time series value. The values for series_id are “actual” for the original series and “synthetic_n” (where n is an integer) for the generated series. See the Examples section for example output.

Parameters
n_seriesint, optional

The number of bootstraped time series that will be generated, by default 10.

spint, optional

Seasonal periodicity of the data in integer form, by default 12. Must be an integer >= 2

block_lengthint, optional

The length of the block in the MBB method, by default None. If not provided, the following heuristic is used, the block length will the minimum between 2*sp and len(X) - sp.

sampling_replacementbool, optional

Whether the MBB sample is with or without replacement, by default False.

return_actualbool, optional

If True the output will contain the actual time series, by default True. The actual time series will be labelled as “<series_name>_actual” (or “actual” if series name is None).

lambda_boundsTuple, optional

BoxCox parameter: Lower and upper bounds used to restrict the feasible range when solving for the value of lambda, by default None.

lambda_methodstr, optional

BoxCox parameter: {“pearsonr”, “mle”, “all”, “guerrero”}, by default “guerrero”. The optimization approach used to determine the lambda value used in the Box-Cox transformation.

seasonalint, optional

STL parameter: Length of the seasonal smoother. Must be an odd integer, and should normally be >= 7, by default 7.

trendint, optional

STL parameter: Length of the trend smoother, by default None. Must be an odd integer. If not provided uses the smallest odd integer greater than 1.5 * period / (1 - 1.5 / seasonal), following the suggestion in the original implementation.

low_passint, optional

STL parameter: Length of the low-pass filter, by default None. Must be an odd integer >=3. If not provided, uses the smallest odd integer > period

seasonal_degint, optional

STL parameter: Degree of seasonal LOESS. 0 (constant) or 1 (constant and trend), by default 1.

trend_degint, optional

STL parameter: Degree of trend LOESS. 0 (constant) or 1 (constant and trend), by default 1.

low_pass_degint, optional

STL parameter: Degree of low pass LOESS. 0 (constant) or 1 (constant and trend), by default 1.

robustbool, optional

STL parameter: Flag indicating whether to use a weighted version that is robust to some forms of outliers, by default False.

seasonal_jumpint, optional

STL parameter: Positive integer determining the linear interpolation step, by default 1. If larger than 1, the LOESS is used every seasonal_jump points and linear interpolation is between fitted points. Higher values reduce estimation time.

trend_jumpint, optional

STL parameter: Positive integer determining the linear interpolation step, by default 1. If larger than 1, the LOESS is used every trend_jump points and values between the two are linearly interpolated. Higher values reduce estimation time.

low_pass_jumpint, optional

STL parameter: Positive integer determining the linear interpolation step, by default 1. If larger than 1, the LOESS is used every low_pass_jump points and values between the two are linearly interpolated. Higher values reduce estimation time.

inner_iterint, optional

STL parameter: Number of iterations to perform in the inner loop, by default None. If not provided uses 2 if robust is True, or 5 if not. This param goes into STL.fit() from statsmodels.

outer_iterint, optional

STL parameter: Number of iterations to perform in the outer loop, by default None. If not provided uses 15 if robust is True, or 0 if not. This param goes into STL.fit() from statsmodels.

random_stateint, np.random.RandomState or None, by default None

Controls the randomness of the estimator

Attributes
is_fitted

Whether fit has been called.

See also

sktime.transformations.bootstrap.MovingBlockBootstrapTransformer

Transofrmer that applies the Moving Block Bootstrapping method to create a panel of synthetic time series.

References

1

Bergmeir, C., Hyndman, R. J., & Benítez, J. M. (2016). Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. International Journal of Forecasting, 32(2), 303-312

2

Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3, Chapter 12.5. Accessed on February 13th 2022.

3

Kunsch HR (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17(3), 1217-1241

4

https://www.statsmodels.org/dev/generated/statsmodels.tsa.seasonal.STL.html

Examples

>>> from sktime.transformations.bootstrap import STLBootstrapTransformer
>>> from sktime.datasets import load_airline
>>> from sktime.utils.plotting import plot_series  
>>> y = load_airline()  
>>> transformer = STLBootstrapTransformer(10)  
>>> y_hat = transformer.fit_transform(y)  
>>> series_list = []  
>>> names = []  
>>> for group, series in y_hat.groupby(level=[0], as_index=False):
...     series.index = series.index.droplevel(0)
...     series_list.append(series)
...     names.append(group)  
>>> plot_series(*series_list, labels=names)  
(...)
>>> print(y_hat.head())  
                      Number of airline passengers
series_id time_index
actual    1949-01                            112.0
          1949-02                            118.0
          1949-03                            132.0
          1949-04                            129.0
          1949-05                            121.0

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/mirror 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 tag value from estimator class (only class tags).

get_class_tags()

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

get_fitted_params()

Get fitted parameters.

get_param_defaults()

Get parameter defaults for the object.

get_param_names()

Get parameter names for the object.

get_params([deep])

Get parameters for this 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])

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

set_params(**params)

Set the parameters of this object.

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. Equal in value to type(self)(**self.get_params(deep=False)).

Returns
instance of type(self), clone of self (see above)
clone_tags(estimator, tag_names=None)[source]#

clone/mirror 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__}

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.

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
XSeries or Panel, any supported mtype
Data to fit transform to, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

subject to sktime mtype format specifications, for further details see

examples/AA_datatypes_and_datasets.ipynb

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

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
XSeries or Panel, any supported mtype
Data to be transformed, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

subject to sktime mtype format specifications, for further details see

examples/AA_datatypes_and_datasets.ipynb

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

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 tag value from estimator class (only class tags).

Parameters
tag_namestr

Name of tag value.

tag_value_defaultany type

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 estimator class and all its parent classes.

Returns
collected_tagsdict

Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or mirror_tags.

get_fitted_params()[source]#

Get fitted parameters.

Overrides BaseEstimator default in case of vectorization.

State required:

Requires state to be “fitted”.

Returns
fitted_paramsdict of fitted parameters, keys are str names of parameters

parameters of components are indexed as [componentname]__[paramname]

classmethod get_param_defaults()[source]#

Get parameter defaults for the object.

Returns
default_dict: dict with str keys

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 parameter names for the object.

Returns
param_names: list of str, alphabetically sorted list of parameter names of cls
get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters
deepbool, default=True

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

Returns
paramsdict

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_value

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
XSeries or Panel, any supported mtype
Data to be inverse transformed, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

subject to sktime mtype format specifications, for further details see

examples/AA_datatypes_and_datasets.ipynb

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

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.

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

Detail behaviour: removes any object attributes, except:

hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)[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/.

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

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

BaseObject parameters

Returns
selfreference to self (after parameters have been set)
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 settting 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
XSeries or Panel, any supported mtype
Data to be transformed, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

subject to sktime mtype format specifications, for further details see

examples/AA_datatypes_and_datasets.ipynb

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

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
XSeries or Panel, any supported mtype
Data to fit transform to, of python type as follows:

Series: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) Panel: pd.DataFrame with 2-level MultiIndex, list of pd.DataFrame,

nested pd.DataFrame, or pd.DataFrame in long/wide format

subject to sktime mtype format specifications, for further details see

examples/AA_datatypes_and_datasets.ipynb

ySeries or Panel, default=None

Additional data, e.g., labels for transformation

update_paramsbool, default=True

whether the model is updated. Yes if true, if false, simply skips call. argument exists for compatibility with forecasting module.

Returns
selfa fitted instance of the estimator