PytorchForecastingDeepAR#
- class PytorchForecastingDeepAR(model_params: dict[str, Any] | None = None, allowed_encoder_known_variable_names: list[str] | None = None, dataset_params: dict[str, Any] | None = None, train_to_dataloader_params: dict[str, Any] | None = None, validation_to_dataloader_params: dict[str, Any] | None = None, trainer_params: dict[str, Any] | None = None, model_path: str | None = None, deterministic: bool = False, random_log_path: bool = False, broadcasting: bool = False)[source]#
pytorch-forecasting DeepAR model.
- Parameters:
- model_paramsdict[str, Any] (default=None)
parameters to be passed to initialize the pytorch-forecasting NBeats model [1] for example: {“cell_type”: “GRU”, “rnn_layers”: 3}
- dataset_paramsdict[str, Any] (default=None)
parameters to initialize TimeSeriesDataSet [2] from pandas.DataFrame max_prediction_length will be overwrite according to fh time_idx, target, group_ids, time_varying_known_reals, time_varying_unknown_reals will be infered from data, so you do not have to pass them
- train_to_dataloader_paramsdict[str, Any] (default=None)
parameters to be passed for TimeSeriesDataSet.to_dataloader() by default {“train”: True}
- validation_to_dataloader_paramsdict[str, Any] (default=None)
parameters to be passed for TimeSeriesDataSet.to_dataloader() by default {“train”: False}
- model_path: string (default=None)
try to load a existing model without fitting. Calling the fit function is still needed, but no real fitting will be performed.
- deterministic: bool (default=False)
set seed before predict, so that it will give the same output for the same input
- random_log_path: bool (default=False)
use random root directory for logging. This parameter is for CI test in Github action, not designed for end users.
- Attributes:
- algorithm_class
Import underlying pytorch-forecasting algorithm class.
- algorithm_parameters
Get keyword parameters for the DeepAR class.
- dict
keyword arguments for the underlying algorithm class
cutoff
Cut-off = “present time” state of forecaster.
fh
Forecasting horizon that was passed.
is_fitted
Whether
fit
has been called.
References
[1]Examples
>>> # import packages >>> from sktime.forecasting.base import ForecastingHorizon >>> from sktime.forecasting.pytorchforecasting import PytorchForecastingDeepAR >>> from sktime.utils._testing.hierarchical import _make_hierarchical >>> from sklearn.model_selection import train_test_split >>> # generate random data >>> data = _make_hierarchical( ... hierarchy_levels=(5, 200), max_timepoints=50, min_timepoints=50, n_columns=3 ... ) >>> # define forecast horizon >>> max_prediction_length = 5 >>> fh = ForecastingHorizon(range(1, max_prediction_length + 1), is_relative=True) >>> # split X, y data for train and test >>> x = data["c0", "c1"] >>> y = data["c2"].to_frame() >>> X_train, X_test, y_train, y_test = train_test_split( ... x, y, test_size=0.2, train_size=0.8, shuffle=False ... ) >>> len_levels = len(y_test.index.names) >>> y_test = y_test.groupby(level=list(range(len_levels - 1))).apply( ... lambda x: x.droplevel(list(range(len_levels - 1))).iloc[:-max_prediction_length] ... ) >>> # define the model >>> model = PytorchForecastingDeepAR( ... trainer_params={ ... "max_epochs": 5, # for quick test ... "limit_train_batches": 10, # for quick test ... }, ... ) >>> # fit and predict >>> model.fit(y=y_train, X=X_train, fh=fh) # doctest skip PytorchForecastingDeepAR(trainer_params={'limit_train_batches': 10, 'max_epochs': 5}) >>> y_pred = model.predict(fh, X=X_test, y=y_test) >>> print(y_test) c2 h0 h1 time h0_0 h1_180 2000-01-01 5.006716 2000-01-02 5.197903 2000-01-03 4.477552 2000-01-04 4.751521 2000-01-05 3.323994 ... ... h0_4 h1_199 2000-02-10 5.590399 2000-02-11 5.595445 2000-02-12 4.915307 2000-02-13 4.726925 2000-02-14 5.482842
[4500 rows x 1 columns] >>> print(y_pred)
c2
h0 h1 time h0_0 h1_180 2000-02-15 4.919366
2000-02-16 4.862666 2000-02-17 5.021425 2000-02-18 4.934844 2000-02-19 4.808967
… … h0_4 h1_199 2000-02-15 5.150748
2000-02-16 5.230827 2000-02-17 5.123736 2000-02-18 5.139505 2000-02-19 5.121511
[500 rows x 1 columns]
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
(y[, X, fh])Fit forecaster to training data.
fit_predict
(y[, X, fh, X_pred])Fit and forecast time series at future horizon.
get_class_tag
(tag_name[, tag_value_default])Get class tag value from class, with tag level inheritance from parents.
Get class tags from class, with tag level inheritance from parent classes.
Get config flags for self.
get_fitted_params
([deep])Get fitted parameters.
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.
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.
predict
([fh, X, y])Forecast time series at future horizon.
predict_interval
([fh, X, coverage, y])Compute/return prediction interval forecasts.
predict_proba
([fh, X, marginal, y])Compute/return fully probabilistic forecasts.
predict_quantiles
([fh, X, alpha, y])Compute/return quantile forecasts.
predict_residuals
([y, X])Return residuals of time series forecasts.
predict_var
([fh, X, cov, y])Compute/return variance forecasts.
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.
score
(y[, X, fh])Scores forecast against ground truth, using MAPE (non-symmetric).
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.
update
(y[, X, update_params])Update cutoff value and, optionally, fitted parameters.
update_predict
(y[, cv, X, update_params, ...])Make predictions and update model iteratively over the test set.
update_predict_single
([y, fh, X, update_params])Update model with new data and make forecasts.
- property algorithm_parameters: dict[source]#
Get keyword parameters for the DeepAR class.
- Returns:
- dict
keyword arguments for the underlying algorithm class
- 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)
orMyClass(**params[i])
creates a valid test instance.create_test_instance
uses the first (or only) dictionary inparams
- check_is_fitted(method_name=None)[source]#
Check if the estimator has been fitted.
Check if
_is_fitted
attribute is present andTrue
. Theis_fitted
attribute should be set toTrue
in calls to an object’sfit
method.If not, raises a
NotFittedError
.- Parameters:
- method_namestr, optional
Name of the method that called this function. If provided, the error message will include this information.
- Raises:
- NotFittedError
If the estimator has not been fitted yet.
- clone()[source]#
Obtain a clone of the object with same hyper-parameters and config.
A clone is a different object without shared references, in post-init state. This function is equivalent to returning
sklearn.clone
ofself
.Equivalent to constructing a new instance of
type(self)
, with parameters ofself
, that is,type(self)(**self.get_params(deep=False))
.If configs were set on
self
, the clone will also have the same configs as the original, equivalent to callingcloned_self.set_config(**self.get_config())
.Also equivalent in value to a call of
self.reset
, with the exception thatclone
returns a new object, instead of mutatingself
likereset
.- Raises:
- RuntimeError if the clone is non-conforming, due to faulty
__init__
.
- RuntimeError if the clone is non-conforming, due to faulty
- clone_tags(estimator, tag_names=None)[source]#
Clone tags from another object as dynamic override.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.clone_tags
sets dynamic tag overrides from another object,estimator
.The
clone_tags
method should be called only in the__init__
method of an object, during construction, or directly after construction via__init__
.The dynamic tags are set to the values of the tags in
estimator
, with the names specified intag_names
.The default of
tag_names
writes all tags fromestimator
toself
.Current tag values can be inspected by
get_tags
orget_tag
.- Parameters:
- estimatorAn instance of :class:BaseObject or derived class
- tag_namesstr or list of str, default = None
Names of tags to clone. The default (
None
) clones all tags fromestimator
.
- Returns:
- self
Reference to
self
.
- classmethod create_test_instance(parameter_set='default')[source]#
Construct an instance of the class, using first test parameter set.
- 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
- 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. The naming convention is
{cls.__name__}-{i}
if more than one instance, otherwise{cls.__name__}
- property cutoff[source]#
Cut-off = “present time” state of forecaster.
- Returns:
- cutoffpandas compatible index element, or None
pandas compatible index element, if cutoff has been set; None otherwise
- fit(y, X=None, fh=None)[source]#
Fit forecaster to training data.
- 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 toTrue
.Sets
self.cutoff
to last index seen iny
.Stores
fh
toself.fh
iffh
is passed.
- Parameters:
- ytime series in
sktime
compatible data container format. Time series to which to fit the forecaster.
Individual data formats in
sktime
are so-called mtype specifications, each mtype implements an abstract scitype.Series
scitype = individual time series, vanilla forecasting.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series, global/panel forecasting.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection, for hierarchical forecasting.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype. For usage, see forecasting tutorial
examples/01_forecasting.ipynb
- fhint, list, pd.Index coercible, or
ForecastingHorizon
, default=None The forecasting horizon encoding the time stamps to forecast at. If
self.get_tag("requires-fh-in-fit")
isTrue
, must be passed infit
, not optional- Xtime series in
sktime
compatible format, optional (default=None). Exogeneous time series to fit the model to. Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containy.index
.
- ytime series in
- Returns:
- selfReference to self.
- fit_predict(y, X=None, fh=None, X_pred=None)[source]#
Fit and forecast time series at future horizon.
Same as
fit(y, X, fh).predict(X_pred)
. IfX_pred
is not passed, same asfit(y, fh, X).predict(X)
.- 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 toTrue
.Sets
self.cutoff
to last index seen iny
.Stores
fh
toself.fh
.
- Parameters:
- ytime series in sktime compatible data container format
Time series to which to fit the forecaster.
Individual data formats in
sktime
are so-called mtype specifications, each mtype implements an abstract scitype.Series
scitype = individual time series, vanilla forecasting.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series, global/panel forecasting.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection, for hierarchical forecasting.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype. For usage, see forecasting tutorial
examples/01_forecasting.ipynb
- fhint, list, pd.Index coercible, or
ForecastingHorizon
(not optional) The forecasting horizon encoding the time stamps to forecast at.
If fh is not None and not of type ForecastingHorizon it is coerced to ForecastingHorizon via a call to _check_fh. In particular, if fh is of type pd.Index it is coerced via ForecastingHorizon(fh, is_relative=False)
- Xtime series in
sktime
compatible format, optional (default=None). Exogeneous time series to fit the model to. Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containy.index
.- X_predtime series in sktime compatible format, optional (default=None)
Exogeneous time series to use in prediction. If passed, will be used in predict instead of X. Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
infit
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containfh
index reference.
- Returns:
- y_predtime series in sktime compatible data container format
Point forecasts at
fh
, with same index asfh
.y_pred
has same type as they
that has been passed most recently:Series
,Panel
,Hierarchical
scitype, same format (see above)
- classmethod get_class_tag(tag_name, tag_value_default=None)[source]#
Get class tag value from class, with tag level inheritance from parents.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.The
get_class_tag
method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.It returns the value of the tag with name
tag_name
from the object, taking into account tag overrides, in the following order of descending priority:Tags set in the
_tags
attribute of the class.Tags set in the
_tags
attribute of parent classes,
in order of inheritance.
Does not take into account dynamic tag overrides on instances, set via
set_tags
orclone_tags
, that are defined on instances.To retrieve tag values with potential instance overrides, use the
get_tag
method instead.- 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 inself
. If not found, returnstag_value_default
.
- classmethod get_class_tags()[source]#
Get class tags from class, with tag level inheritance from parent classes.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.The
get_class_tags
method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.It returns a dictionary with keys being keys of any attribute of
_tags
set in the class or any of its parent classes.Values are the corresponding tag values, with overrides in the following order of descending priority:
Tags set in the
_tags
attribute of the class.Tags set in the
_tags
attribute of parent classes,
in order of inheritance.
Instances can override these tags depending on hyper-parameters.
To retrieve tags with potential instance overrides, use the
get_tags
method instead.Does not take into account dynamic tag overrides on instances, set via
set_tags
orclone_tags
, that are defined on instances.For including overrides from dynamic tags, use
get_tags
.- Returns:
- collected_tagsdict
Dictionary of tag name : tag value pairs. Collected from
_tags
class attribute via nested inheritance. NOT overridden by dynamic tags set byset_tags
orclone_tags
.
- get_config()[source]#
Get config flags for self.
Configs are key-value pairs of
self
, typically used as transient flags for controlling behaviour.get_config
returns dynamic configs, which override the default configs.Default configs are set in the class attribute
_config
of the class or its parent classes, and are overridden by dynamic configs set viaset_config
.Configs are retained under
clone
orreset
calls.- 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 objectif
deep=True
, also contains keys/value pairs of component parameters parameters of components are indexed as[componentname]__[paramname]
all parameters ofcomponentname
appear asparamname
with its valueif
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
. Ifsort=False
, in same order as they appear in the class__init__
. Ifsort=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 adict
of parameter name : value for this object, including parameters of components (=BaseObject
-valued parameters).If
False
, will return adict
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 constructionif
deep=True
, also contains keys/value pairs of component parameters parameters of components are indexed as[componentname]__[paramname]
all parameters ofcomponentname
appear asparamname
with its valueif
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 instance, with tag level inheritance and overrides.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.The
get_tag
method retrieves the value of a single tag with nametag_name
from the instance, taking into account tag overrides, in the following order of descending priority:Tags set via
set_tags
orclone_tags
on the instance,
at construction of the instance.
Tags set in the
_tags
attribute of the class.Tags set in the
_tags
attribute of parent classes,
in order of inheritance.
- 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 inself
. If not found, raises an error ifraise_error
is True, otherwise it returnstag_value_default
.
- Raises:
- ValueError, if
raise_error
isTrue
. The
ValueError
is then raised iftag_name
is not inself.get_tags().keys()
.
- ValueError, if
- get_tags()[source]#
Get tags from instance, with tag level inheritance and overrides.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.The
get_tags
method returns a dictionary of tags, with keys being keys of any attribute of_tags
set in the class or any of its parent classes, or tags set viaset_tags
orclone_tags
.Values are the corresponding tag values, with overrides in the following order of descending priority:
Tags set via
set_tags
orclone_tags
on the instance,
at construction of the instance.
Tags set in the
_tags
attribute of the class.Tags set in the
_tags
attribute of parent classes,
in order of inheritance.
- 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.
- 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
BaseObject
descendant instances.
- property is_fitted[source]#
Whether
fit
has been called.Inspects object’s
_is_fitted` attribute that should initialize to ``False
during object construction, and be set to True in calls to an object’s fit method.- Returns:
- bool
Whether the estimator has been fit.
- 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
, ofcls.save(path)
- deserialized self resulting in output at
- classmethod load_from_serial(serial)[source]#
Load object from serialized memory container.
- Parameters:
- serial1st element of output of
cls.save(None)
- serial1st element of output of
- Returns:
- deserialized self resulting in output
serial
, ofcls.save(None)
- deserialized self resulting in output
- predict(fh=None, X=None, y=None)[source]#
Forecast time series at future horizon.
- State required:
Requires state to be “fitted”, i.e.,
self.is_fitted=True
.
Accesses in self:
Fitted model attributes ending in “_”.
self.cutoff
,self.is_fitted
- Writes to self:
Stores
fh
toself.fh
iffh
is passed and has not been passed previously.
- Parameters:
- fhint, list, pd.Index coercible, or
ForecastingHorizon
, default=None The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in
fit
. If has not been passed in fit, must be passed, not optional- Xtime series in
sktime
compatible format, optional (default=None) Exogeneous time series to use in prediction. Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
infit
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containfh
index reference. Ify
is not passed (not performing global forecasting),X
should only contain the time points to be predicted. Ify
is passed (performing global forecasting),X
must contain all historical values and the time points to be predicted.- ytime series in
sktime
compatible format, optional (default=None) Historical values of the time series that should be predicted. If not None, global forecasting will be performed. Only pass the historical values not the time points to be predicted.
- fhint, list, pd.Index coercible, or
- Returns:
- y_predtime series in sktime compatible data container format
Point forecasts at
fh
, with same index asfh
.y_pred
has same type as they
that has been passed most recently:Series
,Panel
,Hierarchical
scitype, same format (see above)
Notes
If
y
is not None, global forecast will be performed. In global forecast mode,X
should contain all historical values and the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.If
y
is None, non global forecast will be performed. In non global forecast mode,X
should only contain the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.
- predict_interval(fh=None, X=None, coverage=0.9, y=None)[source]#
Compute/return prediction interval forecasts.
If
coverage
is iterable, multiple intervals will be calculated.- State required:
Requires state to be “fitted”, i.e.,
self.is_fitted=True
.
Accesses in self:
Fitted model attributes ending in “_”.
self.cutoff
,self.is_fitted
- Writes to self:
Stores
fh
toself.fh
iffh
is passed and has not been passed previously.
- Parameters:
- fhint, list, np.array or
ForecastingHorizon
, optional (default=None) The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in
fit
. If has not been passed in fit, must be passed, not optional- Xtime series in
sktime
compatible format, optional (default=None) Exogeneous time series to use in prediction. Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
infit
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containfh
index reference. Ify
is passed (performing global forecasting),X
must contain all historical values and the time points to be predicted.- coveragefloat or list of float of unique values, optional (default=0.90)
nominal coverage(s) of predictive interval(s)
- ytime series in
sktime
compatible format, optional (default=None) Historical values of the time series that should be predicted. If not None, global forecasting will be performed. Only pass the historical values not the time points to be predicted.
- fhint, list, np.array or
- Returns:
- pred_intpd.DataFrame
- Column has multi-index: first level is variable name from y in fit,
- second level coverage fractions for which intervals were computed.
in the same order as in input
coverage
.
Third level is string “lower” or “upper”, for lower/upper interval end.
- Row index is fh, with additional (upper) levels equal to instance levels,
from y seen in fit, if y seen in fit was Panel or Hierarchical.
- Entries are forecasts of lower/upper interval end,
for var in col index, at nominal coverage in second col index, lower/upper depending on third col index, for the row index. Upper/lower interval end forecasts are equivalent to quantile forecasts at alpha = 0.5 - c/2, 0.5 + c/2 for c in coverage.
Notes
If
y
is not None, global forecast will be performed. In global forecast mode,X
should contain all historical values and the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.If
y
is None, non global forecast will be performed. In non global forecast mode,X
should only contain the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.
- predict_proba(fh=None, X=None, marginal=True, y=None)[source]#
Compute/return fully probabilistic forecasts.
Note: currently only implemented for Series (non-panel, non-hierarchical) y.
- State required:
Requires state to be “fitted”, i.e.,
self.is_fitted=True
.
Accesses in self:
Fitted model attributes ending in “_”.
self.cutoff
,self.is_fitted
- Writes to self:
Stores
fh
toself.fh
iffh
is passed and has not been passed previously.
- Parameters:
- fhint, list, np.array or
ForecastingHorizon
, optional (default=None) The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in
fit
. If has not been passed in fit, must be passed, not optional- Xtime series in
sktime
compatible format, optional (default=None) Exogeneous time series to use in prediction. Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
infit
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containfh
index reference. Ify
is passed (performing global forecasting),X
must contain all historical values and the time points to be predicted.- marginalbool, optional (default=True)
whether returned distribution is marginal by time index
- ytime series in
sktime
compatible format, optional (default=None) Historical values of the time series that should be predicted. If not None, global forecasting will be performed. Only pass the historical values not the time points to be predicted.
- fhint, list, np.array or
- Returns:
- pred_distsktime BaseDistribution
predictive distribution if marginal=True, will be marginal distribution by time point if marginal=False and implemented by method, will be joint
Notes
If
y
is not None, global forecast will be performed. In global forecast mode,X
should contain all historical values and the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.If
y
is None, non global forecast will be performed. In non global forecast mode,X
should only contain the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.
- predict_quantiles(fh=None, X=None, alpha=None, y=None)[source]#
Compute/return quantile forecasts.
If
alpha
is iterable, multiple quantiles will be calculated.- State required:
Requires state to be “fitted”, i.e.,
self.is_fitted=True
.
Accesses in self:
Fitted model attributes ending in “_”.
self.cutoff
,self.is_fitted
- Writes to self:
Stores
fh
toself.fh
iffh
is passed and has not been passed previously.
- Parameters:
- fhint, list, np.array or
ForecastingHorizon
, optional (default=None) The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in
fit
. If has not been passed in fit, must be passed, not optional- Xtime series in
sktime
compatible format, optional (default=None) Exogeneous time series to use in prediction. Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
infit
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containfh
index reference. Ify
is passed (performing global forecasting),X
must contain all historical values and the time points to be predicted.- alphafloat or list of float of unique values, optional (default=[0.05, 0.95])
A probability or list of, at which quantile forecasts are computed.
- ytime series in
sktime
compatible format, optional (default=None) Historical values of the time series that should be predicted. If not None, global forecasting will be performed. Only pass the historical values not the time points to be predicted.
- fhint, list, np.array or
- Returns:
- quantilespd.DataFrame
- Column has multi-index: first level is variable name from y in fit,
second level being the values of alpha passed to the function.
- Row index is fh, with additional (upper) levels equal to instance levels,
from y seen in fit, if y seen in fit was Panel or Hierarchical.
- Entries are quantile forecasts, for var in col index,
at quantile probability in second col index, for the row index.
Notes
If
y
is not None, global forecast will be performed. In global forecast mode,X
should contain all historical values and the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.If
y
is None, non global forecast will be performed. In non global forecast mode,X
should only contain the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.
- predict_residuals(y=None, X=None)[source]#
Return residuals of time series forecasts.
Residuals will be computed for forecasts at y.index.
If fh must be passed in fit, must agree with y.index. If y is an np.ndarray, and no fh has been passed in fit, the residuals will be computed at a fh of range(len(y.shape[0]))
- State required:
Requires state to be “fitted”. If fh has been set, must correspond to index of y (pandas or integer)
- Accesses in self:
Fitted model attributes ending in “_”. self.cutoff, self._is_fitted
- Writes to self:
Nothing.
- Parameters:
- ytime series in sktime compatible data container format
Time series with ground truth observations, to compute residuals to. Must have same type, dimension, and indices as expected return of predict.
If None, the y seen so far (self._y) are used, in particular:
if preceded by a single fit call, then in-sample residuals are produced
if fit requires
fh
, it must have pointed to index of y in fit
- Xtime series in sktime compatible format, optional (default=None)
Exogeneous time series for updating and forecasting Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
infit
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must contain bothfh
index reference andy.index
.
- Returns:
- y_restime series in
sktime
compatible data container format Forecast residuals at
fh`, with same index as ``fh
.y_res
has same type as they
that has been passed most recently:Series
,Panel
,Hierarchical
scitype, same format (see above)
- y_restime series in
- predict_var(fh=None, X=None, cov=False, y=None)[source]#
Compute/return variance forecasts.
- State required:
Requires state to be “fitted”, i.e.,
self.is_fitted=True
.
Accesses in self:
Fitted model attributes ending in “_”.
self.cutoff
,self.is_fitted
- Writes to self:
Stores
fh
toself.fh
iffh
is passed and has not been passed previously.
- Parameters:
- fhint, list, np.array or
ForecastingHorizon
, optional (default=None) The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in
fit
. If has not been passed in fit, must be passed, not optional- Xtime series in
sktime
compatible format, optional (default=None) Exogeneous time series to use in prediction. Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
infit
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containfh
index reference.- covbool, optional (default=False)
if True, computes covariance matrix forecast. if False, computes marginal variance forecasts.
- ytime series in
sktime
compatible format, optional (default=None) Historical values of the time series that should be predicted. If not None, global forecasting will be performed. Only pass the historical values not the time points to be predicted.
- fhint, list, np.array or
- Returns:
- pred_varpd.DataFrame, format dependent on
cov
variable - If cov=False:
- Column names are exactly those of
y
passed infit
/update
. For nameless formats, column index will be a RangeIndex.
- Row index is fh, with additional levels equal to instance levels,
from y seen in fit, if y seen in fit was Panel or Hierarchical.
Entries are variance forecasts, for var in col index. A variance forecast for given variable and fh index is a predicted
variance for that variable and index, given observed data.
- Column names are exactly those of
- If cov=True:
- Column index is a multiindex: 1st level is variable names (as above)
2nd level is fh.
- Row index is fh, with additional levels equal to instance levels,
from y seen in fit, if y seen in fit was Panel or Hierarchical.
- Entries are (co-)variance forecasts, for var in col index, and
covariance between time index in row and col.
Note: no covariance forecasts are returned between different variables.
- pred_varpd.DataFrame, format dependent on
Notes
If
y
is not None, global forecast will be performed. In global forecast mode,X
should contain all historical values and the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.If
y
is None, non global forecast will be performed. In non global forecast mode,X
should only contain the time points to be predicted, whiley
should only contain historical values not the time points to be predicted.
- reset()[source]#
Reset the object to a clean post-init state.
Results in setting
self
to the state it had directly after the constructor call, with the same hyper-parameters. Config values set byset_config
are also retained.A
reset
call deletes any object attributes, except:hyper-parameters = arguments of
__init__
written toself
, e.g.,self.paramname
whereparamname
is an argument of__init__
object attributes containing double-underscores, i.e., the string “__”. For instance, an attribute named “__myattr” is retained.
config attributes, configs are retained without change. That is, results of
get_config
before and afterreset
are equal.
Class and object methods, and class attributes are also unaffected.
Equivalent to
clone
, with the exception thatreset
mutatesself
instead of returning a new object.After a
self.reset()
call,self
is equal in value and state, to the object obtained after a constructor call``type(self)(**self.get_params(deep=False))``.- Returns:
- self
Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.
- 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 ifpath
is a file location, stores self at that location as a zip filesaved 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 fileestimator.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
- if
- score(y, X=None, fh=None)[source]#
Scores forecast against ground truth, using MAPE (non-symmetric).
- Parameters:
- ypd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
Time series to score
- fhint, list, pd.Index coercible, or
ForecastingHorizon
, default=None The forecasting horizon encoding the time stamps to forecast at.
- Xpd.DataFrame, or 2D np.array, optional (default=None)
Exogeneous time series to score if self.get_tag(“X-y-must-have-same-index”), X.index must contain y.index
- Returns:
- scorefloat
MAPE loss of self.predict(fh, X) with respect to y_test.
- 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
, requiresdask
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 forjoblib.Parallel
can be passed here, e.g.,n_jobs
, with the exception ofbackend
which is directly controlled bybackend
. Ifn_jobs
is not passed, it will default to-1
, other parameters will default tojoblib
defaults.“joblib”: custom and 3rd party
joblib
backends, e.g.,spark
. Any valid keys forjoblib.Parallel
can be passed here, e.g.,n_jobs
,backend
must be passed as a key ofbackend_params
in this case. Ifn_jobs
is not passed, it will default to-1
, other parameters will default tojoblib
defaults.“dask”: any valid keys for
dask.compute
can be passed, e.g.,scheduler
- remember_databool, default=True
whether self._X and self._y are stored in fit, and updated in update. If True, self._X and self._y are stored and updated. If False, self._X and self._y are not stored and updated. This reduces serialization size when using save, but the update will default to “do nothing” rather than “refit to all data seen”.
- 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 skbase objects 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 viaself.get_params
, and sets them to integers derived fromrandom_state
viaset_params
. These integers are sampled from chain hashing viasample_dependent_seed
, and guarantee pseudo-random independence of seeded random generators.Applies to
random_state
parameters inself
, depending onself_policy
, and remaining component objects if and only ifdeep=True
.Note: calls
set_params
even ifself
does not have arandom_state
, or none of the components have arandom_state
parameter. Therefore,set_random_state
will reset anyscikit-base
object, even those without arandom_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 skbase object valued parameters, i.e., component estimators.
If False, will set only
self
’srandom_state
parameter, if exists.If True, will set
random_state
parameters in component objects as well.
- self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”
“copy” :
self.random_state
is set to inputrandom_state
“keep” :
self.random_state
is kept as is“new” :
self.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 instance level tag overrides to given values.
Every
scikit-base
compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.Tags are key-value pairs specific to an instance
self
, they are static flags that are not changed after construction of the object.set_tags
sets dynamic tag overrides to the values as specified intag_dict
, with keys being the tag name, and dict values being the value to set the tag to.The
set_tags
method should be called only in the__init__
method of an object, during construction, or directly after construction via__init__
.Current tag values can be inspected by
get_tags
orget_tag
.- Parameters:
- **tag_dictdict
Dictionary of tag name: tag value pairs.
- Returns:
- Self
Reference to self.
- update(y, X=None, update_params=True)[source]#
Update cutoff value and, optionally, fitted parameters.
If no estimator-specific update method has been implemented, default fall-back is as follows:
update_params=True
: fitting to all observed data so farupdate_params=False
: updates cutoff and remembers data only
- State required:
Requires state to be “fitted”, i.e.,
self.is_fitted=True
.
Accesses in self:
Fitted model attributes ending in “_”.
self.cutoff
,self.is_fitted
Writes to self:
Updates
self.cutoff
to latest index seen iny
.If
update_params=True
, updates fitted model attributes ending in “_”.
- Parameters:
- ytime series in
sktime
compatible data container format. Time series with which to update the forecaster.
Individual data formats in
sktime
are so-called mtype specifications, each mtype implements an abstract scitype.Series
scitype = individual time series, vanilla forecasting.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series, global/panel forecasting.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection, for hierarchical forecasting.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype. For usage, see forecasting tutorial
examples/01_forecasting.ipynb
- Xtime series in
sktime
compatible format, optional (default=None). Exogeneous time series to update the model fit with Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containy.index
.- update_paramsbool, optional (default=True)
whether model parameters should be updated. If
False
, only the cutoff is updated, model parameters (e.g., coefficients) are not updated.
- ytime series in
- Returns:
- selfreference to self
- update_predict(y, cv=None, X=None, update_params=True, reset_forecaster=True)[source]#
Make predictions and update model iteratively over the test set.
Shorthand to carry out chain of multiple
update
/predict
executions, with data playback based on temporal splittercv
.Same as the following (if only
y
,cv
are non-default):self.update(y=cv.split_series(y)[0][0])
remember
self.predict()
(return later in single batch)self.update(y=cv.split_series(y)[1][0])
remember
self.predict()
(return later in single batch)etc
return all remembered predictions
If no estimator-specific update method has been implemented, default fall-back is as follows:
update_params=True
: fitting to all observed data so farupdate_params=False
: updates cutoff and remembers data only
- State required:
Requires state to be “fitted”, i.e.,
self.is_fitted=True
.
Accesses in self:
Fitted model attributes ending in “_”.
self.cutoff
,self.is_fitted
- Writes to self (unless
reset_forecaster=True
): Updates
self.cutoff
to latest index seen iny
.If
update_params=True
, updates fitted model attributes ending in “_”.
Does not update state if
reset_forecaster=True
.- Parameters:
- ytime series in
sktime
compatible data container format. Time series with which to update the forecaster.
Individual data formats in
sktime
are so-called mtype specifications, each mtype implements an abstract scitype.Series
scitype = individual time series, vanilla forecasting.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series, global/panel forecasting.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection, for hierarchical forecasting.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype. For usage, see forecasting tutorial
examples/01_forecasting.ipynb
- cvtemporal cross-validation generator inheriting from BaseSplitter, optional
for example,
SlidingWindowSplitter
orExpandingWindowSplitter
; default = ExpandingWindowSplitter withinitial_window=1
and defaults = individual data points in y/X are added and forecast one-by-one,initial_window = 1
,step_length = 1
andfh = 1
- Xtime series in sktime compatible format, optional (default=None)
Exogeneous time series for updating and forecasting Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
infit
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containfh
index reference.- update_paramsbool, optional (default=True)
whether model parameters should be updated. If
False
, only the cutoff is updated, model parameters (e.g., coefficients) are not updated.- reset_forecasterbool, optional (default=True)
if True, will not change the state of the forecaster, i.e., update/predict sequence is run with a copy, and cutoff, model parameters, data memory of self do not change
if False, will update self when the update/predict sequence is run as if update/predict were called directly
- ytime series in
- Returns:
- y_predobject that tabulates point forecasts from multiple split batches
format depends on pairs (cutoff, absolute horizon) forecast overall
if collection of absolute horizon points is unique: type is time series in sktime compatible data container format cutoff is suppressed in output has same type as the y that has been passed most recently: Series, Panel, Hierarchical scitype, same format (see above)
if collection of absolute horizon points is not unique: type is a pandas DataFrame, with row and col index being time stamps row index corresponds to cutoffs that are predicted from column index corresponds to absolute horizons that are predicted entry is the point prediction of col index predicted from row index entry is nan if no prediction is made at that (cutoff, horizon) pair
- update_predict_single(y=None, fh=None, X=None, update_params=True)[source]#
Update model with new data and make forecasts.
This method is useful for updating and making forecasts in a single step.
If no estimator-specific update method has been implemented, default fall-back is first update, then predict.
- State required:
Requires state to be “fitted”.
- Accesses in self:
Fitted model attributes ending in “_”. Pointers to seen data, self._y and self.X self.cutoff, self._is_fitted If update_params=True, model attributes ending in “_”.
- Writes to self:
Update self._y and self._X with
y
andX
, by appending rows. Updates self.cutoff and self._cutoff to last index seen iny
. If update_params=True,updates fitted model attributes ending in “_”.
- Parameters:
- ytime series in
sktime
compatible data container format. Time series with which to update the forecaster.
Individual data formats in
sktime
are so-called mtype specifications, each mtype implements an abstract scitype.Series
scitype = individual time series, vanilla forecasting.pd.DataFrame
,pd.Series
, ornp.ndarray
(1D or 2D)Panel
scitype = collection of time series, global/panel forecasting.pd.DataFrame
with 2-level rowMultiIndex
(instance, time)
,3D np.ndarray
(instance, variable, time)
,list
ofSeries
typedpd.DataFrame
Hierarchical
scitype = hierarchical collection, for hierarchical forecasting.pd.DataFrame
with 3 or more level rowMultiIndex
(hierarchy_1, ..., hierarchy_n, time)
For further details on data format, see glossary on mtype. For usage, see forecasting tutorial
examples/01_forecasting.ipynb
- fhint, list, pd.Index coercible, or
ForecastingHorizon
, default=None The forecasting horizon encoding the time stamps to forecast at. Should not be passed if has already been passed in
fit
. If has not been passed in fit, must be passed, not optional- Xtime series in sktime compatible format, optional (default=None)
Exogeneous time series for updating and forecasting Should be of same scitype (
Series
,Panel
, orHierarchical
) asy
infit
. Ifself.get_tag("X-y-must-have-same-index")
,X.index
must containfh
index reference.- update_paramsbool, optional (default=True)
whether model parameters should be updated. If
False
, only the cutoff is updated, model parameters (e.g., coefficients) are not updated.
- ytime series in
- Returns:
- y_predtime series in sktime compatible data container format
Point forecasts at
fh
, with same index asfh
.y_pred
has same type as they
that has been passed most recently:Series
,Panel
,Hierarchical
scitype, same format (see above)