Merger

Merger

class Merger(method='median', stride=0)[source]

Aggregates Panel data containing overlapping windows of one time series.

The input data contains multiple overlapping time series elements that could arranged as follows: xxxx….. .xxxx…. ..xxxx… …xxxx.. ….xxxx. …..xxxx ……xxxx …….xxxx ……..xxxx ………xxxx The merger aggregates the data by aligning the time series windows as shown above and applying a aggregation function to the overlapping data points. The aggregation function can be one of “mean” or “median”. I.e., the mean or median of each column is calculated, resulting in a univariate time series.

Parameters:

method{median, mean}, default=”median”

The method to use for aggregation. Can be one of “mean” or “median”.

strideint, default=0

The stride to use for the aggregation. The stride determines the number of shifts between consecutive instances. A stride of 0 means no shift. A stride of 1 means that the time series is aggregated as above.

Attributes:

is_fitted

Whether fit has been called.

Examples

>>> from sktime.transformations.merger import Merger
>>> from sktime.utils._testing.panel import _make_panel
>>> y = _make_panel(n_instances=10, n_columns=3, n_timepoints=5)
>>> result = Merger(method="median").fit_transform(y)
>>> result.shape
(5, 3)

>>> from sktime.transformations.merger import Merger
>>> from sktime.utils._testing.panel import _make_panel
>>> y = _make_panel(n_instances=10, n_columns=3, n_timepoints=5)
>>> result = Merger(method="median", stride=1).fit_transform(y)
>>> result.shape
(14, 3)

Methods

check_is_fitted([method_name])	Check if the estimator has been fitted.
clone()	Obtain a clone of the object with same hyper-parameters and config.
clone_tags(estimator[, tag_names])	Clone tags from another object as dynamic override.
create_test_instance([parameter_set])	Construct an instance of the class, using first test parameter set.
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 class tag value from class, with tag level inheritance from parents.
get_class_tags()	Get class tags from class, with tag level inheritance from 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 instance, with tag level inheritance and overrides.
get_tags()	Get tags from instance, with tag level inheritance and 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 instance level tag overrides to given values.
transform(X[, y])	Transform X and return a transformed version.
update(X[, y, update_params])	Update transformer with X, optionally y.