MSTLSeriesTransformer

class MSTLSeriesTransformer(periods: Sequence[int], iterate: int = 2, *, s_windows: Sequence[int | None] | None = None, trend: int | None = None, low_pass: int | None = None, seasonal_deg: int = 1, trend_deg: int = 1, robust: bool = False, seasonal_jump: int = 1, trend_jump: int = 1, inner_iter: int | None = None, outer_iter: int | None = None, boxcox_lambda: float | None = None, impute_missing: bool = True, output: str = 'remainder')

Bases: BaseSeriesTransformer

Multiple Seasonal-Trend decomposition using Loess (MSTL) for a single series.

Implements the MSTL algorithm described by Bandara, Hyndman & Bergmeir (2021)[1], extending STL to multiple seasonalities via iterative STL fits. See Algorithm 1 and Appendix A in the paper for defaults of seasonal windows. The R reference is forecast::mstl.

The algorithm iteratively applies STL per seasonal period (smallest to largest), adding back the previously stored seasonal for the current period before refitting, and then subtracting the new estimate (Algorithm 1). The trend is taken from the last STL fit in the final outer iteration.

Parameters:

periodsSequence[int]

Seasonal periods to extract (ascending order is not required).

iterateint, default=2

Number of outer iterations over the list of seasonalities.

s_windowsSequence[Optional[int]] or None, default=None

Seasonal LOESS windows (odd ints) for each period. If None, defaults are s_window[i] = smallest odd >= 7 + 4*(i+1) i=0..m-1 i.e., 11, 15, 19, …

trendOptional[int], default=None

STL trend window (odd int, > period). If None, STL default is used per period.

low_passOptional[int], default=None

STL internal low-pass window. If None, STL default is used per period.

seasonal_deg{0,1}, default=1

Seasonal LOESS degree for STL calls.

trend_deg{0,1}, default=1

Trend LOESS degree for STL calls.

robustbool, default=False

Enable STL robustness iterations in each inner STL.

seasonal_jumpint, default=1

Evaluate seasonal LOESS every seasonal_jump points in STL

(knot interpolation).

trend_jumpint, default=1

Evaluate trend LOESS every trend_jump points in STL (knot interpolation).

inner_iterOptional[int], default=None

STL inner iterations per STL call; defaults to 2 if robust else 5.

outer_iterOptional[int], default=None

STL robustness iterations per STL call; defaults to 15 if robust else 0.

boxcox_lambdaOptional[float], default=None

If given, apply Box–Cox transform before decomposition (strictly positive data). lambda=0 uses log transform.

impute_missingbool, default=True

Linearly interpolate NaNs before decomposition.

output{“remainder”,”trend”,”seasonal_sum”,”seasonals”,”all”}, default=”remainder”

• “remainder”: (n,)

• “trend”: (n,)

• “seasonal_sum”: sum of all seasonal components, (n,)

• “seasonals”: (n, m) matrix with one column per period (ascending)

• “all”: (n, m+2) matrix with columns [*seasonals, trend, remainder]

Notes

Capabilities

Missing Values	Yes
Multithreading	No
Inverse Transform	No
Univariate	Yes
Multivariate	No

References

[1]

Bandara, K., Hyndman, R.J., & Bergmeir, C. (2021).

“MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns.” arXiv:2107.13462.

Methods

clone([random_state])	Obtain a clone of the object with the same hyperparameters.
fit(X[, y, axis])	Fit transformer to X, optionally using y if supervised.
fit_transform(X[, y, axis])	Fit to data, then transform it.
get_class_tag(tag_name[, raise_error, ...])	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([deep])	Get fitted parameters.
get_params([deep])	Get parameters for this estimator.
get_tag(tag_name[, raise_error, ...])	Get tag value from estimator class.
get_tags()	Get tags from estimator.
reset([keep])	Reset the object to a clean post-init state.
set_fit_request(*[, axis])	Configure whether metadata should be requested to be passed to the fit method.
set_params(**params)	Set the parameters of this estimator.
set_tags(**tag_dict)	Set dynamic tags to given values.
set_transform_request(*[, axis])	Configure whether metadata should be requested to be passed to the transform method.
transform(X[, y, axis])	Transform X and return a transformed version.

clone(random_state=None)

Obtain a clone of the object with the same hyperparameters.

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)).

Parameters:

random_stateint, RandomState instance, or None, default=None

Sets the random state of the clone. If None, the random state is not set. If int, random_state is the seed used by the random number generator. If RandomState instance, random_state is the random number generator.

Returns:

estimatorobject

Instance of type(self), clone of self (see above)

fit(X, y=None, axis=1)

Fit transformer to X, optionally using y if supervised.

Writes to self: - is_fitted : flag is set to True. - model attributes (ending in “_”) : dependent on estimator

Parameters:

XInput data

Time series to fit transform to, of type np.ndarray, pd.Series pd.DataFrame.

yTarget variable, default=None

Additional data, e.g., labels for transformation

axisint, default = 1

Axis of time in the input series. If axis == 0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis == 1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints)`.``axis is None indicates that the axis of X is the same as self.axis.

Returns:

selfa fitted instance of the estimator

fit_transform(X, y=None, axis=1)

Fit to data, then transform it.

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

Changes state to “fitted”. Model attributes (ending in “_”) : dependent on estimator.

Parameters:

XInput data

Data to fit transform to, of valid collection type.

yTarget variable, default=None

Additional data, e.g., labels for transformation

axisint, default = 1

Axis of time in the input series. If axis == 0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis == 1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints)`.``axis is None indicates that the axis of X is the same as self.axis.

Returns:

transformed version of X with the same axis as passed by the user, if axis

not None.

classmethod get_class_tag(tag_name, raise_error=True, tag_value_default=None)

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr

Name of tag value.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

Returns:

tag_value

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

Raises:

ValueError

if raise_error is True and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> DummyClassifier.get_class_tag("capability:multivariate")
True

classmethod get_class_tags()

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

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance. These are not overridden by dynamic tags set by set_tags or class __init__ calls.

get_fitted_params(deep=True)

Get fitted parameters.

State required:

Requires state to be “fitted”.

Parameters:

deepbool, default=True

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

Returns:

fitted_paramsdict

Fitted parameter names mapped to their values.

get_params(deep=True)

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, raise_error=True, tag_value_default=None)

Get tag value from estimator class.

Includes dynamic and overridden tags.

Parameters:

tag_namestr

Name of tag to be retrieved.

raise_errorbool, default=True

Whether a ValueError is raised when the tag is not found.

tag_value_defaultany type, default=None

Default/fallback value if tag is not found and error is not raised.

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 and tag_name is not in self.get_tags().keys()

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> d.get_tag("capability:multivariate")
True

get_tags()

Get tags from estimator.

Includes dynamic and overridden tags.

Returns:

collected_tagsdict

Dictionary of tag name and tag value pairs. Collected from _tags class attribute via nested inheritance and then any overridden and new tags from __init__ or set_tags.

reset(keep=None)

Reset the object to a clean post-init state.

After a self.reset() call, self is equal or similar in value to type(self)(**self.get_params(deep=False)), assuming no other attributes were kept using keep.

Detailed 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 hyperparameters (result of get_params)

Not affected by the reset are:

object attributes containing double-underscores class and object methods, class attributes any attributes specified in the keep argument temporary callback context attached by scikit-learn

Parameters:

keepNone, str, or list of str, default=None

If None, all attributes are removed except hyperparameters. If str, only the attribute with this name is kept. If list of str, only the attributes with these names are kept.

Returns:

selfobject

Reference to self.

Raises:

TypeError

If ‘keep’ is not a string or a list of strings.

set_fit_request(*, axis: bool | None | str = '$UNCHANGED$') → MSTLSeriesTransformer

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in fit.

Returns:

selfobject

The updated object.

set_params(**params)

Set the parameters of this estimator.

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

Parameters:

**paramsdict

Estimator parameters.

Returns:

selfestimator instance

Estimator instance.

set_tags(**tag_dict)

Set dynamic tags to given values.

Parameters:

**tag_dictdict

Dictionary of tag name and tag value pairs.

Returns:

selfobject

Reference to self.

set_transform_request(*, axis: bool | None | str = '$UNCHANGED$') → MSTLSeriesTransformer

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to transform.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

axisstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for axis parameter in transform.

Returns:

selfobject

The updated object.

transform(X, y=None, axis=1)

Transform X and return a transformed version.

State required:

Requires state to be “fitted”.

Parameters:

XInput data

Data to fit transform to, of valid collection type.

yTarget variable, default=None

Additional data, e.g., labels for transformation

axisint, default = 1

Axis of time in the input series. If axis == 0, it is assumed each column is a time series and each row is a time point. i.e. the shape of the data is (n_timepoints, n_channels). axis == 1 indicates the time series are in rows, i.e. the shape of the data is (n_channels, n_timepoints)`.``axis is None indicates that the axis of X is the same as self.axis.

Returns:

transformed version of X with the same axis as passed by the user, if axis

not None.