DartsXGBModel#

Darts XGBModel Estimator.

This is based on implementation of XGBoost Model in darts [1] by Unit8.

Parameters:

lagsOne of int, list, dict, default=None

Lagged target values used to predict the next time step. If an integer is given the last lags past lags are used (from -1 backward). Otherwise a list of integers with lags is required (each lag must be < 0). If a dictionary is given, keys correspond to the component names (of first series when using multiple series) and the values correspond to the component lags(integer or list of integers).

lags_past_covariatesOne of int, list, dict, default=None

Number of lagged past_covariates values used to predict the next time step. If an integer is given the last lags_past_covariates past lags are used (inclusive, starting from lag -1). Otherwise a list of integers with lags < 0 is required. If a dictionary is given, keys correspond to the past_covariates component names(of first series when using multiple series) and the values correspond to the component lags(integer or list of integers).

lags_future_covariatesOne of tuple, list, dict, default=None

Number of lagged future_covariates values used to predict the next time step. If a tuple (past, future) is given the last past lags in the past are used (inclusive, starting from lag -1) along with the first future future lags (starting from 0 - the prediction time - up to future - 1 included). Otherwise a list of integers with lags is required. If dictionary is given, keys correspond to the future_covariates component names (of first series when using multiple series) and the values correspond to the component lags(integer or list of integers).

output_chunk_lengthint, default=1

Number of time steps predicted at once by the internal regression model. Does not have to equal the forecast horizon n used in predict(). However, setting output_chunk_length equal to the forecast horizon may be useful if the covariates don’t extend far enough into the future.

add_encodersdict, default=None

A large number of past and future covariates can be automatically generated with add_encoders. This can be done by adding multiple pre-defined index encoders and/or custom user-made functions that will be used as index encoders. Additionally, a transformer such as Darts’ Scaler can be added to transform the generated covariates. This happens all under one hood and only needs to be specified at model creation. Read SequentialEncoder to find out more about add_encoders. Default: None. An example showing some of add_encoders features:

add_encoders={
    'cyclic': {'future': ['month']},
    'datetime_attribute': {'future': ['hour', 'dayofweek']},
    'position': {'past': ['relative'], 'future': ['relative']},
    'custom': {'past': [lambda idx: (idx.year - 1950) / 50]},
    'transformer': Scaler()
}

likelihoodstr, default=None

Can be set to poisson or quantile. If set, the model will be probabilistic, allowing sampling at prediction time. This will overwrite any objective parameter.

quantileslist, default=None

Fit the model to these quantiles if the likelihood is set to quantile.

random_stateint, default=None

Control the randomness in the fitting procedure and for sampling. Default: None.

multi_modelsbool, default=True

If True, a separate model will be trained for each future lag to predict. If False, a single model is trained to predict at step ‘output_chunk_length’ in the future. Default: True.

use_static_covariatesbool, default=True

Whether the model should use static covariate information in case the input series passed to fit() contain static covariates. If True, and static covariates are available at fitting time, will enforce that all target series have the same static covariate dimensionality in fit() and predict().

past_covariateslist, default=None

column names in X which are known only for historical data, by default None

num_samplesint, default=1000

Number of times a prediction is sampled from a probabilistic model, by default 1000

kwargsdict, default=None

Additional keyword arguments passed to xgb.XGBRegressor. Passed as a dictionary to conform to sklearn’s API. Default: None.

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.

Notes

If unspecified, all columns will be assumed to be known during prediction duration.

References

[1]

unit8co/darts

Methods

`check_is_fitted`()	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters.
`clone_tags`(estimator[, tag_names])	Clone tags from another estimator as dynamic override.
`convert_dataframe_to_timeseries`(dataset)	Convert dataset for compatibility with `darts`.
`convert_exogenous_dataset`(dataset)	Make exogenous features to `darts` compatible, if available.
`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, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get a class tag's value.
`get_class_tags`()	Get class tags from the class and all its parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from estimator class and dynamic tag overrides.
`get_tags`()	Get tags from estimator class and dynamic tag overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`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`()	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 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. There are currently no reserved values for forecasters.

Returns:

paramsdict or list of dict, default = {}: Parameters to create testing instances of the class

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.

Raises:

RuntimeError if the clone is non-conforming, due to faulty __init__.

Notes

If successful, equal in value to type(self)(**self.get_params(deep=False)).

clone_tags(estimator, tag_names=None)[source]#

Clone tags from another estimator as dynamic override.

Parameters:

estimatorestimator 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.

static convert_dataframe_to_timeseries(dataset: DataFrame)[source]#

Convert dataset for compatibility with darts.

Parameters:

datasetpandas.DataFrame: source dataset to convert from

Returns:

darts.TimeSeries: converted target dataset

convert_exogenous_dataset(dataset: DataFrame | None)[source]#

Make exogenous features to darts compatible, if available.

Parameters:

datasetOptional[pandas.DataFrame]: available data on exogenous features

Returns:

Tuple[darts.TimeSeries, darts.TimeSeries]: converted data on future known and future unknown exogenous features

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__}

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 fitted model attributes ending in “_”, fitted attributes are inspectable via get_fitted_params.

Sets self.is_fitted flag to True.

Sets self.cutoff to last index seen in y.

Stores fh to self.fh if fh 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, or np.ndarray (1D or 2D)
Panel scitype = collection of time series, global/panel forecasting. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection, for hierarchical forecasting. pd.DataFrame with 3 or more level row MultiIndex (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") is True, must be passed in fit, 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, or Hierarchical) as y. If self.get_tag("X-y-must-have-same-index"), X.index must contain y.index.

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). If X_pred is not passed, same as fit(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 to True.

Sets self.cutoff to last index seen in y.

Stores fh to self.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, or np.ndarray (1D or 2D)
Panel scitype = collection of time series, global/panel forecasting. pd.DataFrame with 2-level row MultiIndex (instance, time), 3D np.ndarray (instance, variable, time), list of Series typed pd.DataFrame
Hierarchical scitype = hierarchical collection, for hierarchical forecasting. pd.DataFrame with 3 or more level row MultiIndex (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, or Hierarchical) as y. If self.get_tag("X-y-must-have-same-index"), X.index must contain y.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, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.

Returns:

y_predtime series in sktime 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 scitype, same format (see above)

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get a class tag’s value.

Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Parameters:

tag_namestr: Name of tag value.
tag_value_defaultany: Default/fallback value if tag is not found.

Returns:

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

classmethod get_class_tags()[source]#

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

Retrieves tag: value pairs from _tags class attribute. Does not return information from dynamic tags (set via set_tags or clone_tags) that are defined on instances.

Returns:

collected_tagsdict: Dictionary of class tag name: tag value pairs. Collected from _tags class attribute via nested inheritance.

get_config()[source]#

Get config flags for self.

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 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

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. If sort=False, in same order as they appear in the class __init__. If sort=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 a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).
If False, will return a dict 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 construction
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_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

Get tag value from estimator class and dynamic tag overrides.

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 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()[source]#

Get tags from estimator class and dynamic tag overrides.

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 BaseObjects.

property is_fitted[source]#: Whether fit has been called.

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, of cls.save(path)

classmethod load_from_serial(serial)[source]#

Load object from serialized memory container.

Parameters:

serial1st 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”, i.e., self.is_fitted=True.

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, 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

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 use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.

Returns:

y_predtime series in sktime 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 scitype, 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”, i.e., self.is_fitted=True.

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, 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

If fh is not None and not of type ForecastingHorizon, it is coerced to ForecastingHorizon internally (via _check_fh).

if fh is int or array-like of int, it is interpreted as relative horizon, and coerced to a relative ForecastingHorizon(fh, is_relative=True).
if fh is of type pd.Index, it is interpreted as an absolute horizon, and coerced to an absolute ForecastingHorizon(fh, is_relative=False).

Xtime series in sktime compatible format, optional (default=None)

Exogeneous time series to use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.

coveragefloat or list of float of unique values, optional (default=0.90)

nominal coverage(s) of predictive interval(s)

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.

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”, i.e., self.is_fitted=True.

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, 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

If fh is not None and not of type ForecastingHorizon, it is coerced to ForecastingHorizon internally (via _check_fh).

if fh is int or array-like of int, it is interpreted as relative horizon, and coerced to a relative ForecastingHorizon(fh, is_relative=True).
if fh is of type pd.Index, it is interpreted as an absolute horizon, and coerced to an absolute ForecastingHorizon(fh, is_relative=False).

Xtime series in sktime compatible format, optional (default=None)

Exogeneous time series to use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.

marginalbool, optional (default=True)

whether returned distribution is marginal by time index

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

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”, i.e., self.is_fitted=True.

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, 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

If fh is not None and not of type ForecastingHorizon, it is coerced to ForecastingHorizon internally (via _check_fh).

if fh is int or array-like of int, it is interpreted as relative horizon, and coerced to a relative ForecastingHorizon(fh, is_relative=True).
if fh is of type pd.Index, it is interpreted as an absolute horizon, and coerced to an absolute ForecastingHorizon(fh, is_relative=False).

Xtime series in sktime compatible format, optional (default=None)

Exogeneous time series to use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.

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.

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 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, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain both fh index reference and y.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 the y that has been passed most recently: Series, Panel, Hierarchical scitype, same format (see above)

predict_var(fh=None, X=None, cov=False)[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 to self.fh if fh 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

If fh is not None and not of type ForecastingHorizon, it is coerced to ForecastingHorizon internally (via _check_fh).

if fh is int or array-like of int, it is interpreted as relative horizon, and coerced to a relative ForecastingHorizon(fh, is_relative=True).
if fh is of type pd.Index, it is interpreted as an absolute horizon, and coerced to an absolute ForecastingHorizon(fh, is_relative=False).

Xtime series in sktime compatible format, optional (default=None)

Exogeneous time series to use in prediction. Should be of same scitype (Series, Panel, or Hierarchical) as y in fit. If self.get_tag("X-y-must-have-same-index"), X.index must contain fh index reference.

covbool, optional (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.

Using reset, runs __init__ with current values of hyper-parameters (result of get_params). This Removes any object attributes, except:

hyper-parameters = arguments of __init__

object attributes containing double-underscores, i.e., the string “__”

Class and object methods, and class attributes are also unaffected.

Returns:

self: Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

Notes

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

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 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/.
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

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.