PanelGluontsList

class PanelGluontsList(is_univariate=None, is_equally_spaced=None, is_equal_length=None, is_empty=None, is_one_series=None, has_nans=None, n_instances=None, n_features=None, feature_names=None, dtypekind_dfip=None, feature_kind=None)[source]

Data type: gluonTS representation of univariate and multivariate time series.

Name: "gluonts_ListDataset_panel"

Short description:

A list of dict, with list index = instances, dict['target'] rows = time points, dict['target'] cols = variables, and dict['start'] marking the interval. Identical to gluonts.dataset.common.ListDataset.

Long description:

The "gluonts_ListDataset_panel" mtype is a concrete specification that implements the Panel scitype, i.e., the abstract type of a collection of time series.

An object obj: list follows the specification iff:

structure convention: obj must be a list of dict. Each dict must contain a key "target"``which maps to a 1D ``numpy.ndarray for a univariate, and 2D numpy.ndarray for a multivariate time series. Optionally, it may also contain a key "start" that maps to a pandas.Period object. eg: pandas.Period("2024-01-01", freq="D") for a time series starting on 2024-01-01 and sampled daily.
instances: instances correspond to different list elements of obj.
instance index: the instance index of an instance is the list index at which it is located in obj. That is, the data at obj[i] correspond to observations of the instance with index i.
time points: rows of obj[i]['target'] correspond to different, distinct time points, at which instance i is observed.
time index: the time index is implicit and by-convention. The j-th element (for an integer j) of instance i is interpreted as an observation at the time point j. If "start" key is present, the time index for the j-th element of instance i is obj[i]['start'] + j.
variables: columns of obj[i]['target'] correspond to different variables available for instance i.
variable names: numpy mtypes cannot represent variable names. If required, then variable names are assigned "value_{k} where k is the feature column index.

Capabilities:

can represent panels of multivariate series
can not represent panels of unequally spaced series
can represent panels of unequally supported series
can represent panels of series with different sets of variables
can represent missing values

Parameters:

is_univariate: bool: True iff table has one variable
is_equally_spacedbool: True iff series index is equally spaced
is_equal_length: bool: True iff all series in panel are of equal length
is_empty: bool: True iff table has no variables or no instances
is_one_series: bool: True iff there is only one series in the panel of time series
has_nans: bool: True iff the table contains NaN values
n_instances: int: number of instances in the panel of time series
n_features: int: number of variables in table
feature_names: list of int or object: names of variables in table
dtypekind_dfip: list of DtypeKind enum: list of DtypeKind enum values for each feature in the panel, following the data frame interface protocol
feature_kind: list of str: list of feature kind strings for each feature in the panel, coerced to FLOAT or CATEGORICAL type

Methods

`__call__`(obj[, return_metadata, var_name, ...])	Check if obj is of this data type.
`check`(obj[, return_metadata, var_name, ...])	Check if obj is of this data type.
`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.
`get`(key[, default])	Get attribute by key.
`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_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 skbase object.
`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.