LTSFNLinearForecaster#
- class LTSFNLinearForecaster(seq_len, pred_len, *, num_epochs=16, batch_size=8, in_channels=1, individual=False, criterion=None, criterion_kwargs=None, optimizer=None, optimizer_kwargs=None, lr=0.001, custom_dataset_train=None, custom_dataset_pred=None)[source]#
LTSF-NLinear Forecaster.
Implementation of the Long-Term Short-Term Feature (LTSF) normalization linear forecaster, aka LTSF-NLinear, by Zeng et al [1].
Core logic is directly copied from the cure-lab LTSF-Linear implementation [R658c94d8ec96-2], which is unfortunately not available as a package.
- Parameters:
- seq_lenint
length of input sequence
- pred_lenint
length of prediction (forecast horizon)
- num_epochsint, default=16
number of epochs to train
- batch_sizeint, default=8
number of training examples per batch
- in_channelsint, default=1
number of input channels passed to network
- individualbool, default=False
boolean flag that controls whether the network treats each channel individually” “or applies a single linear layer across all channels. If individual=True, the” “a separate linear layer is created for each input channel. If” “individual=False, a single shared linear layer is used for all channels.”
- criteriontorch.nn Loss Function, default=torch.nn.MSELoss
loss function to be used for training
- criterion_kwargsdict, default=None
keyword arguments to pass to criterion
- optimizertorch.optim.Optimizer, default=torch.optim.Adam
optimizer to be used for training
- optimizer_kwargsdict, default=None
keyword arguments to pass to optimizer
- lrfloat, default=0.003
learning rate to train model with
- Attributes:
References
[1]Zeng A, Chen M, Zhang L, Xu Q. 2023.
Are transformers effective for time series forecasting? Proceedings of the AAAI conference on artificial intelligence 2023 (Vol. 37, No. 9, pp. 11121-11128). .. [R658c94d8ec96-2] cure-lab/LTSF-Linear
Examples
>>> from sktime.forecasting.ltsf import LTSFNLinearForecaster >>> from sktime.datasets import load_airline >>> model = LTSFNLinearForecaster(10, 3) >>> y = load_airline() >>> model.fit(y, fh=[1,2,3]) LTSFNLinearForecaster(pred_len=3, seq_len=10) >>> y_pred = model.predict() >>> y_pred 1961-01 455.628082 1961-02 433.349640 1961-03 437.045502 Freq: M, Name: Number of airline passengers, dtype: float32
Methods
Build PyTorch DataLoader for prediction.
Build PyTorch DataLoader for training.
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.
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 from the class and all its 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 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.
get_y_true
(y)Get y_true values for validation.
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.
- 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.
- Raises:
- RuntimeError if the clone is non-conforming, due to faulty
__init__
.
- RuntimeError if the clone is non-conforming, due to faulty
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.
- 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
- 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, np.array or ForecastingHorizon, optional (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, np.array or
ForecastingHorizon
(not optional) The forecasting horizon encoding the time stamps to forecast at.
- 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 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 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. If
sort=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 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.
- 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)[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, 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.
- fhint, list, np.array 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)
- 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
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.- coveragefloat or list of float of unique values, optional (default=0.90)
nominal coverage(s) of predictive interval(s)
- 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.
- 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
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.- marginalbool, optional (default=True)
whether returned distribution is marginal by time index
- 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
- 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
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.- 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.
- 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.
- 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)[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.
- 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
- 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 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, array-like or ForecastingHorizon, optional (default=None)
The forecasters horizon with the steps ahead to to predict.
- 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 estimators 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 viaestimator.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 inestimator
depending onself_policy
, and remaining component estimators 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
estimator, 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 sub-estimators. If False, will set only
self
’srandom_state
parameter, if exists. If True, will setrandom_state
parameters in sub-estimators as well.- self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”
“copy” :
estimator.random_state
is set to inputrandom_state
“keep” :
estimator.random_state
is kept as is“new” :
estimator.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 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 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, 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 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)