# NaiveVariance¶

class NaiveVariance(forecaster, initial_window=1, verbose=False)[source]

Compute the prediction variance based on a naive strategy.

NaiveVariance adds to a forecaster the ability to compute the prediction variance based on naive assumptions about the time series. The simple strategy is as follows: - Let $$y_1,\dots,y_T$$ be the time series we fit the estimator $$f$$ to. - Let $$\widehat{y}_{ij}$$ be the forecast for time point $$j$$, obtained from fitting the forecaster to the partial time series $$y_1,\dots,y_i$$. - We compute the residuals matrix $$R=(r_{ij})=(y_j-\widehat{y}_{ij})$$. - The variance prediction $$v_k$$ for $$y_{T+k}$$ is $$\frac{1}{T-k}\sum_{i=1}^{T-k} a_{i,i+k}^2$$ because we are averaging squared residuals of all forecasts that are $$k$$ time points ahead. - And for the covariance matrix prediction, the formula becomes $$Cov(y_k, y_l)=\frac{\sum_{i=1}^N \hat{r}_{k,k+i}*\hat{r}_{l,l+i}}{N}$$.

The resulting forecaster will implement

predict_interval, predict_quantiles, predict_var, and predict_proba, even if the wrapped forecaster forecaster did not have this capability; for point forecasts (predict), behaves like the wrapped forecaster.

Parameters:
forecasterestimator

Estimator to which probabilistic forecasts are being added

initial_windowint, optional, default=1

number of minimum initial indices to use for fitting when computing residuals

verbosebool, optional, default=False

whether to print warnings if windows with too few data points occur

Attributes:
cutoff

Cut-off = “present time” state of forecaster.

fh

Forecasting horizon that was passed.

is_fitted

Whether fit has been called.

Examples

>>> from aeon.datasets import load_airline
>>> from aeon.forecasting.naive import NaiveForecaster, NaiveVariance
>>> forecaster = NaiveForecaster(strategy="drift")
>>> variance_forecaster = NaiveVariance(forecaster)
>>> variance_forecaster.fit(y)
NaiveVariance(...)
>>> var_pred = variance_forecaster.predict_var(fh=[1,2,3])


Methods

 Check if the estimator has been fitted. 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(y[, X, fh]) Fit forecaster to training data. fit_predict(y[, X, fh]) Fit and forecast time series at future horizon. get_class_tag(tag_name[, tag_value_default]) Get tag value from estimator class (only class tags). Get class tags from estimator class and all its parent classes. get_fitted_params([deep]) Get fitted parameters. Get metadata routing of this object. Get parameter defaults for the object. 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. Get tags from estimator class. get_test_params([parameter_set]) Return testing parameter settings for the estimator. 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. predict([fh, X]) Forecast time series at future horizon. predict_interval([fh, X, coverage]) Compute/return prediction interval forecasts. predict_proba([fh, X, marginal]) Compute/return fully probabilistic forecasts. predict_quantiles([fh, X, alpha]) Compute/return quantile forecasts. predict_residuals([y, X]) Return residuals of time series forecasts. predict_var([fh, X, cov]) Compute/return variance forecasts. Reset the object to a clean post-init state. save([path]) Save serialized self to bytes-like object or to (.zip) file. score(y[, X, fh]) Scores forecast against ground truth, using MAPE. set_fit_request(*[, fh]) Request metadata passed to the fit method. set_params(**params) Set the parameters of this object. set_predict_proba_request(*[, fh, marginal]) Request metadata passed to the predict_proba method. set_predict_request(*[, fh]) Request metadata passed to the predict method. set_score_request(*[, fh]) Request metadata passed to the score method. set_tags(**tag_dict) Set dynamic tags 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.
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
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:
estimatorobject

Estimator 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.

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

property fh[source]

Forecasting horizon that was passed.

fit(y, X=None, fh=None)[source]

Fit forecaster to training data.

State change:

Changes state to “fitted”.

Writes to self:

Sets self._is_fitted flag to True. Writes self._y and self._X with y and X, respectively. Sets self.cutoff and self._cutoff to last index seen in y. Sets fitted model attributes ending in “_”. Stores fh to self.fh if fh is passed.

Parameters:
ytime series in aeon compatible data container format

Time series to which to fit the forecaster.

y can be in one of the following formats: Series abstract type: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)

for vanilla forecasting, one time series

Panel abstract type: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame for global or panel forecasting

Hierarchical abstract type: pd.DataFrame with 3 or more level row MultiIndex

for hierarchical forecasting

Number of columns admissible depend on the “y_input_type” tag:
if self.get_tag(“y_input_type”)==”univariate”:

y must have a single column/variable

if self.get_tag(“y_input_type”)==”multivariate”:

y must have 2 or more columns

if self.get_tag(“y_input_type”)==”both”: no restrictions apply.

For further details:

on usage, see forecasting examples/forecasting on specification of formats, examples/datasets

Xtime series in aeon compatible format, default=None

Exogeneous time series to fit to

Should be of same abstract type (Series, Panel, or Hierarchical) as y if self.get_tag(“X-y-must-have-same-index”), X.index must contain y.index there are no restrictions on number of columns (unlike for y).

fhint, list, np.array or ForecastingHorizon, default=None

The forecasting horizon encoding the time stamps to forecast at. if self.get_tag(“requires-fh-in-fit”), must be passed, not optional.

Returns:
selfReference to self.
fit_predict(y, X=None, fh=None)[source]

Fit and forecast time series at future horizon.

State change:

Changes state to “fitted”.

Writes to self:

Sets is_fitted flag to True. Writes self._y and self._X with y and X, respectively. Sets self.cutoff and self._cutoff to last index seen in y. Sets fitted model attributes ending in “_”. Stores fh to self.fh.

Parameters:
ytime series in aeon compatible data container format

Time series to which to fit the forecaster.

y can be in one of the following formats: Series abstract type: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)

for vanilla forecasting, one time series

Panel abstract type: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame for global or panel forecasting

Hierarchical abstract type: pd.DataFrame with 3 or more level row MultiIndex

for hierarchical forecasting

Number of columns admissible depend on the “y_input_type” tag:
if self.get_tag(“y_input_type”)==”univariate”:

y must have a single column/variable

if self.get_tag(“y_input_type”)==”multivariate”:

y must have 2 or more columns

if self.get_tag(“y_input_type”)==”both”: no restrictions apply

For further details:

on usage, see examples/forecasting on specification of formats, examples/datasets

fhint, list, np.array or ForecastingHorizon (not optional)

The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional

Xtime series in aeon compatible format, default=None

Exogeneous time series to fit to

Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag(“X-y-must-have-same-index”),

X.index must contain fh.index and y.index both

fhint, list, np.array or ForecastingHorizon (not optional)

The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional

Returns:
y_predtime series in aeon compatible data container format

Point forecasts at fh, with same index as fh y_pred has same type as the y that has been passed most recently:

Series, Panel, Hierarchical abstract type, same format (see above)

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

Returns:
tag_value

Value of the tag_name tag in self. If not found, returns tag_value_default.

get_tag

Get a single tag from an object.

get_tags

Get all tags from an object.

get_class_tag

Get a single tag from a class.

Examples

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

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(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 object

• if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value

• if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

A MetadataRequest encapsulating routing information.

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.

Uses dynamic tag overrides.

Parameters:
tag_namestr

Name of tag to be retrieved.

tag_value_defaultany type, default=None

raise_errorbool

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

Get all tags from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

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

get_tags()[source]

Get tags from estimator class.

Includes the dynamic tag overrides.

Returns:
dict

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.

get_tag

Get a single tag from an object.

get_clas_tags

Get all tags from a class.

get_class_tag

Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> tags = d.get_tags()

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.

Parameters:
serialobject

Result of ZipFile(path).open(“object).

Returns:
deserialized self resulting in output at path, of cls.save(path)

Load object from serialized memory container.

Parameters:
serialobject

First element of output of cls.save(None).

Returns:
deserialized self resulting in output serial, of cls.save(None).
predict(fh=None, X=None)[source]

Forecast time series at future horizon.

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self.cutoff, self._is_fitted

Writes to self:

Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:
fhint, list, np.array or ForecastingHorizon, default=None

The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional.

Xtime series in aeon compatible format, default=None

Exogeneous time series to fit to

Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag(“X-y-must-have-same-index”), X.index must contain fh.index there are no restrictions on number of columns (unlike for y).

Returns:
y_predtime series in aeon compatible data container format

Point forecasts at fh, with same index as fh y_pred has same type as the y that has been passed most recently:

Series, Panel, Hierarchical abstract type, same format (see above).

predict_interval(fh=None, X=None, coverage=0.9)[source]

Compute/return prediction interval forecasts.

If coverage is iterable, multiple intervals will be calculated.

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self.cutoff, self._is_fitted

Writes to self:

Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:
fhint, list, np.array or ForecastingHorizon (not optional)

The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional.

Xtime series in aeon compatible format, default=None

Exogeneous time series to fit to

Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag(“X-y-must-have-same-index”), must contain fh.index.

coveragefloat or list of float of unique values, default=0.9

Nominal coverage(s) of predictive interval(s).

Returns:
pd.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.

predict_proba(fh=None, X=None, marginal=True)[source]

Compute/return fully probabilistic forecasts.

Note: currently only implemented for Series (non-panel, non-hierarchical) y.

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self.cutoff, self._is_fitted

Writes to self:

Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:
fhint, list, np.array or ForecastingHorizon

The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional.

Xtime series in aeon compatible format, default=None

Exogeneous time series to fit to.

Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag(“X-y-must-have-same-index”), must contain fh.index.

marginalbool, default=True

Whether returned distribution is marginal by time index.

Returns:
pred_disttfp Distribution object
if marginal=True:

batch shape is 1D and same length as fh event shape is 1D, with length equal number of variables being forecast i-th (batch) distribution is forecast for i-th entry of fh j-th (event) index is j-th variable, order as y in fit/update

if marginal=False:

there is a single batch event shape is 2D, of shape (len(fh), no. variables) i-th (event dim 1) distribution is forecast for i-th entry of fh j-th (event dim 1) index is j-th variable, order as y in fit/update

predict_quantiles(fh=None, X=None, alpha=None)[source]

Compute/return quantile forecasts.

If alpha is iterable, multiple quantiles will be calculated.

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self.cutoff, self._is_fitted

Writes to self:

Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:
fhint, list, np.array or ForecastingHorizon (not optional)

The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional

Xtime series in aeon compatible format, default=None

Exogeneous time series to fit to

Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag(“X-y-must-have-same-index”), must contain fh.index

alphafloat or list of float of unique values, default=[0.05, 0.95]

A probability or list of, at which quantile forecasts are computed.

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.

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 aeon 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.

Xpd.DataFrame, or 2D np.ndarray, default=None

Exogeneous time series to predict from if self.get_tag(“X-y-must-have-same-index”),

X.index must contain fh.index and y.index both.

Returns:
y_restime series in aeon compatible data container format

Forecast residuals at fh, with same index as fh y_res has same type as the y that has been passed most recently:

Series, Panel, Hierarchical abstract type, same format (see above)

predict_var(fh=None, X=None, cov=False)[source]

Compute/return variance forecasts.

State required:

Requires state to be “fitted”.

Accesses in self:

Fitted model attributes ending in “_”. self.cutoff, self._is_fitted

Writes to self:

Stores fh to self.fh if fh is passed and has not been passed previously.

Parameters:
fhint, list, np.array or ForecastingHorizon (not optional)

The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional

Xtime series in aeon compatible format, default=None

Exogeneous time series to fit to

Should be of same abstract type (Series, Panel, or Hierarchical) as y in fit if self.get_tag(“X-y-must-have-same-index”),

X.index must contain fh.index and y.index both

covbool, default=False

if True, computes covariance matrix forecast. if False, computes marginal variance forecasts.

Returns:
pred_varpd.DataFrame, format dependent on cov variable
If cov=False:
Column names are exactly those of y passed in fit/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.

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.

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.
score(y, X=None, fh=None)[source]

Scores forecast against ground truth, using MAPE.

Parameters:
ypd.Series, pd.DataFrame, or np.ndarray (1D or 2D)

Time series to score if self.get_tag(“y_input_type”)==”univariate”:

must have a single column/variable

if self.get_tag(“y_input_type”)==”multivariate”:

must have 2 or more columns.

if self.get_tag(“y_input_type”)==”both”: no restrictions apply

Xpd.DataFrame, or 2D np.array, default=None

Exogeneous time series to score. if self.get_tag(“X-y-must-have-same-index”), X.index must contain y.index

fhint, list, array-like or ForecastingHorizon, default=None

The forecasters horizon with the steps ahead to to predict.

Returns:
scorefloat

sMAPE loss of self.predict(fh, X) with respect to y_test.

set_fit_request(*, fh: = '$UNCHANGED$') [source]

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
fhstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for fh parameter in fit.

Returns:
selfobject

The updated object.

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_predict_proba_request(*, fh: = '$UNCHANGED$', marginal: = '$UNCHANGED$') [source]

Request metadata passed to the predict_proba method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to predict_proba 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 predict_proba.

• 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
fhstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for fh parameter in predict_proba.

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

Metadata routing for marginal parameter in predict_proba.

Returns:
selfobject

The updated object.

set_predict_request(*, fh: = '$UNCHANGED$') [source]

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to predict 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 predict.

• 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
fhstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for fh parameter in predict.

Returns:
selfobject

The updated object.

set_score_request(*, fh: = '$UNCHANGED$') [source]

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to score 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 score.

• 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.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:
fhstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for fh parameter in score.

Returns:
selfobject

The updated object.

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 setting tag values in tag_dict as dynamic tags in 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 far update_params=False: updates cutoff and remembers data only

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 and X, by appending rows. Updates self.cutoff and self._cutoff to last index seen in y. If update_params=True,

updates fitted model attributes ending in “_”.

Parameters:
ytime series in aeon compatible data container format

Time series to which to fit the forecaster in the update.

y can be in one of the following formats, must be same abstract type as in fit: Series abstract type: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)

for vanilla forecasting, one time series

Panel abstract type: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame for global or panel forecasting

Hierarchical abstract type: pd.DataFrame with 3 or more level row MultiIndex

for hierarchical forecasting

Number of columns admissible depend on the “y_input_type” tag:
if self.get_tag(“y_input_type”)==”univariate”:

y must have a single column/variable

if self.get_tag(“y_input_type”)==”multivariate”:

y must have 2 or more columns

if self.get_tag(“y_input_type”)==”both”: no restrictions apply

For further details:

See examples/forecasting, or examples/datasets,

Xtime series in aeon compatible format, default=None

Exogeneous time series to fit to

Should be of same type (Series, Panel, or Hierarchical) as y if self.get_tag(“X-y-must-have-same-index”), X.index must contain y.index there are no restrictions on number of columns (unlike for y)

update_paramsbool, default=True

whether model parameters should be updated

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.

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, if reset_forecaster=False:

Update self._y and self._X with y and X, by appending rows. Updates self.cutoff and self._cutoff to last index seen in y. If update_params=True,

updates fitted model attributes ending in “_”.

Does not update state if reset_forecaster=True.

Parameters:
ytime series in aeon compatible data container format

Time series to which to fit the forecaster in the update.

y can be in one of the following formats, must be same abstract type as in fit: Series abstract type: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) for vanilla forecasting, one time series Panel abstract type: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame for global or panel forecasting

Hierarchical abstract type: pd.DataFrame with 3 or more level row MultiIndex

for hierarchical forecasting

Number of columns admissible depend on the “y_input_type” tag:
if self.get_tag(“y_input_type”)==”univariate”:

y must have a single column/variable

if self.get_tag(“y_input_type”)==”multivariate”:

y must have 2 or more columns

if self.get_tag(“y_input_type”)==”both”: no restrictions apply

For further details see examples/forecasting, or examples/datasets.

cvtemporal cross-validation generator inheriting from BaseSplitter, optional

For example, SlidingWindowSplitter or ExpandingWindowSplitter default = ExpandingWindowSplitter with initial_window=1 and defaults

= individual data points in y/X are added and forecast one-by-one, initial_window = 1, step_length = 1 and fh = 1

Xtime series in aeon compatible format, default=None

Exogeneous time series for updating and forecasting Should be of same abstract type (Series, Panel, or Hierarchical) as y if self.get_tag(“X-y-must-have-same-index”),

X.index must contain y.index and fh.index both

there are no restrictions on number of columns (unlike for y).

update_paramsbool, default=True

Whether model parameters should be updated in each update step.

reset_forecasterbool, 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

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 aeon compatible data container format cutoff is suppressed in output has same type as the y that has been passed most recently: Series, Panel, Hierarchical abstract type, 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 absolut 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 and X, by appending rows. Updates self.cutoff and self._cutoff to last index seen in y. If update_params=True,

updates fitted model attributes ending in “_”.

Parameters:
ytime series in aeon compatible data container format

Time series to which to fit the forecaster in the update. y can be in one of the following formats, must be same abstract type as in fit: Series abstract type: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D) for vanilla forecasting, one time series Panel abstract type: pd.DataFrame with 2-level row MultiIndex,

3D np.ndarray, list of Series pd.DataFrame, or nested pd.DataFrame for global or panel forecasting

Hierarchical abstract type: pd.DataFrame with 3 or more level row MultiIndex

for hierarchical forecasting

Number of columns admissible depend on the “y_input_type” tag:
if self.get_tag(“y_input_type”)==”univariate”:

y must have a single column/variable

if self.get_tag(“y_input_type”)==”multivariate”:

y must have 2 or more columns

if self.get_tag(“y_input_type”)==”both”: no restrictions apply

For further details see examples/forecasting, or examples/datasets.

fhint, list, np.array or ForecastingHorizon, default=None

The forecasting horizon encoding the time stamps to forecast at. if has not been passed in fit, must be passed, not optional.

Xtime series in aeon compatible format, default=None

Exogeneous time series for updating and forecasting

Should be of same abstract type (Series, Panel, or Hierarchical) as y if self.get_tag(“X-y-must-have-same-index”),

X.index must contain y.index and fh.index both.

update_paramsbool, default=False
Returns:
y_predtime series in aeon compatible data container format

Point forecasts at fh, with same index as fh if fh was relative, index is relative to cutoff after update with y y_pred has same type as the y that has been passed most recently:

Series, Panel, Hierarchical abstract type, same format (see above)