VARMAX#
- class VARMAX(order=(1, 0), trend='c', error_cov_type='unstructured', measurement_error=False, enforce_stationarity=True, enforce_invertibility=True, trend_offset=1, start_params=None, transformed=True, includes_fixed=False, cov_type=None, cov_kwds=None, method='lbfgs', maxiter=50, full_output=1, disp=False, callback=None, return_params=False, optim_score=None, optim_complex_step=None, optim_hessian=None, flags=None, low_memory=False, dynamic=False, information_set='predicted', signal_only=False, suppress_warnings=False)[source]#
Wrapper for statsmodels VARMAX model.
Vector Autoregressive Moving Average with eXogenous regressors model (VARMAX)
- Parameters
- orderiterable
The (p,q) order of the model for the number of AR and MA parameters to use.
- trendstr{‘n’,’c’,’t’,’ct’} or iterable, optional
Parameter controlling the deterministic trend polynomial \(A(t)\). Can be specified as a string where ‘c’ indicates a constant (i.e. a degree zero component of the trend polynomial), ‘t’ indicates a linear trend with time, and ‘ct’ is both. Can also be specified as an iterable defining the non-zero polynomial exponents to include, in increasing order. For example, [1,1,0,1] denotes \(a + bt + ct^3\). Default is a constant trend component.
- error_cov_type{‘diagonal’, ‘unstructured’}, optional
The structure of the covariance matrix of the error term, where “unstructured” puts no restrictions on the matrix and “diagonal” requires it to be a diagonal matrix (uncorrelated errors). Default is “unstructured”.
- measurement_errorbool, optional
Whether or not to assume the endogenous observations endog were measured with error. Default is False.
- enforce_stationaritybool, optional
Whether or not to transform the AR parameters to enforce stationarity in the autoregressive component of the model. Default is True.
- enforce_invertibilitybool, optional
Whether or not to transform the MA parameters to enforce invertibility in the moving average component of the model. Default is True.
- trend_offsetint, optional
The offset at which to start time trend values. Default is 1, so that if trend=’t’ the trend is equal to 1, 2, …, n_obs. Typically is only set when the model created by extending a previous dataset.
- start_paramsarray_like, optional
Initial guess of the solution for the loglikelihood maximization. If None, the default is given by Model.start_params.
- transformedbool, optional
Whether or not start_params is already transformed. Default is True.
- includes_fixedbool, optional
If parameters were previously fixed with the fix_params method, this argument describes whether or not start_params also includes the fixed parameters, in addition to the free parameters. Default is False.
- cov_typestr, optional
The cov_type keyword governs the method for calculating the covariance matrix of parameter estimates. Can be one of:
‘opg’ for the outer product of gradient estimator
- ‘oim’ for the observed information matrix estimator, calculated
using the method of Harvey (1989)
- ‘approx’ for the observed information matrix estimator,
calculated using a numerical approximation of the Hessian matrix.
- ‘robust’ for an approximate (quasi-maximum likelihood) covariance
matrix that may be valid even in the presence of some misspecifications. Intermediate calculations use the ‘oim’ method.
- ‘robust_approx’ is the same as ‘robust’ except that the
intermediate calculations use the ‘approx’ method.
‘none’ for no covariance matrix calculation.
Default is ‘opg’ unless memory conservation is used to avoid computing the loglikelihood values for each observation, in which case the default is ‘approx’.
- cov_kwdsdict or None, optional
A dictionary of arguments affecting covariance matrix computation. opg, oim, approx, robust, robust_approx
- ‘approx_complex_step’bool, optional - If True, numerical
approximations are computed using complex-step methods. If False, numerical approximations are computed using finite difference methods. Default is True.
- ‘approx_centered’bool, optional - If True, numerical
approximations computed using finite difference methods use a centered approximation. Default is False.
- methodstr, optional
The method determines which solver from scipy.optimize is used, and it can be chosen from among the following strings:
‘newton’ for Newton-Raphson
‘nm’ for Nelder-Mead
‘bfgs’ for Broyden-Fletcher-Goldfarb-Shanno (BFGS)
‘lbfgs’ for limited-memory BFGS with optional box constraints
‘powell’ for modified Powell’s method
‘cg’ for conjugate gradient
‘ncg’ for Newton-conjugate gradient
‘basinhopping’ for global basin-hopping solver
The explicit arguments in fit are passed to the solver, with the exception of the basin-hopping solver. Each solver has several optional arguments that are not the same across solvers. See the notes section below (or scipy.optimize) for the available arguments and for the list of explicit arguments that the basin-hopping solver supports.
- maxiterint, optional
The maximum number of iterations to perform.
- full_outputbool, optional
Set to True to have all available output in the Results object’s mle_retvals attribute. The output is dependent on the solver. See LikelihoodModelResults notes section for more information.
- dispbool, optional
Set to True to print convergence messages.
- callbackcallable callback(xk), optional
Called after each iteration, as callback(xk), where xk is the current parameter vector.
- return_paramsbool, optional
Whether or not to return only the array of maximizing parameters. Default is False.
- optim_score{‘harvey’, ‘approx’} or None, optional
The method by which the score vector is calculated. ‘harvey’ uses the method from Harvey (1989), ‘approx’ uses either finite difference or complex step differentiation depending upon the value of optim_complex_step, and None uses the built-in gradient approximation of the optimizer. Default is None. This keyword is only relevant if the optimization method uses the score.
- optim_complex_stepbool, optional
Whether or not to use complex step differentiation when approximating the score; if False, finite difference approximation is used. Default is True. This keyword is only relevant if optim_score is set to ‘harvey’ or ‘approx’.
- optim_hessian{‘opg’,’oim’,’approx’}, optional
The method by which the Hessian is numerically approximated. ‘opg’ uses outer product of gradients, ‘oim’ uses the information matrix formula from Harvey (1989), and ‘approx’ uses numerical approximation. This keyword is only relevant if the optimization method uses the Hessian matrix.
- low_memorybool, optional
If set to True, techniques are applied to substantially reduce memory usage. If used, some features of the results object will not be available (including smoothed results and in-sample prediction), although out-of-sample forecasting is possible. Default is False.
- dynamicbool, int, str, or datetime, optional
Integer offset relative to start at which to begin dynamic prediction. Can also be an absolute date string to parse or a datetime type (these are not interpreted as offsets). Prior to this observation, true endogenous values will be used for prediction; starting with this observation and continuing through the end of prediction, forecasted endogenous values will be used instead.
- information_setstr, optional
The information set to condition each prediction on. Default is “predicted”, which computes predictions of period t values conditional on observed data through period t-1; these are one-step-ahead predictions, and correspond with the typical fittedvalues results attribute. Alternatives are “filtered”, which computes predictions of period t values conditional on observed data through period t, and “smoothed”, which computes predictions of period t values conditional on the entire dataset (including also future observations t+1, t+2, …).
- signal_onlybool, optional
Whether to compute predictions of only the “signal” component of the observation equation. Default is False. For example, the observation equation of a time-invariant model is \(y_t = d + Z \alpha_t + \varepsilon_t\), and the “signal” component is then \(Z \alpha_t\). If this argument is set to True, then predictions of the “signal” \(Z \alpha_t\) will be returned. Otherwise, the default is for predictions of \(y_t\) to be returned.
- suppress_warningsbool, optional
Many warnings might be thrown inside of statsmodels. If
suppress_warnings
is True, all of these warnings will be squelched. Default is False.
- Attributes
Notes
Generically, the VARMAX model is specified (see for example chapter 18 of [1]): .. math:
y_t = A(t) + A_1 y_{t-1} + \dots + A_p y_{t-p} + B x_t + \epsilon_t + M_1 \epsilon_{t-1} + \dots M_q \epsilon_{t-q}
where \(\epsilon_t \sim N(0, \Omega)\), and where \(y_t\) is a k_endog x 1 vector. Additionally, this model allows considering the case where the variables are measured with error. Note that in the full VARMA(p,q) case there is a fundamental identification problem in that the coefficient matrices \(\{A_i, M_j\}\) are not generally unique, meaning that for a given time series process there may be multiple sets of matrices that equivalently represent it. See Chapter 12 of [1] for more information. Although this class can be used to estimate VARMA(p,q) models, a warning is issued to remind users that no steps have been taken to ensure identification in this case.
References
- 1(1,2)
Lütkepohl, Helmut. 2007. New Introduction to Multiple Time Series Analysis. Berlin: Springer.
Examples
>>> from sktime.forecasting.varmax import VARMAX >>> from sktime.datasets import load_macroeconomic >>> from sktime.forecasting.model_selection import temporal_train_test_split >>> y = load_macroeconomic() >>> forecaster = VARMAX(suppress_warnings=True) >>> forecaster.fit(y[['realgdp', 'unemp']]) VARMAX(...) >>> y_pred = forecaster.predict(fh=[1,4,12])
Methods
Check if the estimator has been fitted.
clone
()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 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 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.
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
()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_params
(**params)Set the parameters of this object.
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 Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params
- 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
- 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__}
- 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
- 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 sktime compatible data container format
Time series to which to fit the forecaster.
y can be in one of the following formats: Series scitype: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
for vanilla forecasting, one time series
- Panel scitype: 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 scitype: pd.DataFrame with 3 or more level row MultiIndex
for hierarchical forecasting
- Number of columns admissible depend on the “scitype:y” tag:
- if self.get_tag(“scitype:y”)==”univariate”:
y must have a single column/variable
- if self.get_tag(“scitype:y”)==”multivariate”:
y must have 2 or more columns
if self.get_tag(“scitype:y”)==”both”: no restrictions on columns apply
- For further details:
on usage, see forecasting tutorial examples/01_forecasting.ipynb on specification of formats, examples/AA_datatypes_and_datasets.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”), must be passed, not optional
- Xtime series in sktime compatible format, optional (default=None)
Exogeneous time series to fit 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 there are no restrictions on number of columns (unlike for y)
- 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 sktime compatible data container format
Time series to which to fit the forecaster.
y can be in one of the following formats: Series scitype: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
for vanilla forecasting, one time series
- Panel scitype: 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 scitype: pd.DataFrame with 3 or more level row MultiIndex
for hierarchical forecasting
- Number of columns admissible depend on the “scitype:y” tag:
- if self.get_tag(“scitype:y”)==”univariate”:
y must have a single column/variable
- if self.get_tag(“scitype:y”)==”multivariate”:
y must have 2 or more columns
if self.get_tag(“scitype:y”)==”both”: no restrictions on columns apply
- For further details:
on usage, see forecasting tutorial examples/01_forecasting.ipynb on specification of formats, examples/AA_datatypes_and_datasets.ipynb
- 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 sktime compatible format, optional (default=None)
Exogeneous time series to fit to
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 and y.index both
- 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 tag value from estimator class (only class tags).
- Parameters
- tag_namestr
Name of tag value.
- tag_value_defaultany type
Default/fallback value if tag is not found.
- Returns
- tag_value
Value of the tag_name tag in self. If not found, returns tag_value_default.
- 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
- 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 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_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()[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 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
- 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”.
- 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, optional (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 sktime compatible format, optional (default=None)
Exogeneous time series to fit to
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 there are no restrictions on number of columns (unlike for y)
- 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”.
- 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 sktime compatible format, optional (default=None)
Exogeneous time series to fit to
Should be of same scitype (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, 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”.
- 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 sktime compatible format, optional (default=None)
Exogeneous time series to fit to
Should be of same scitype (Series, Panel, or Hierarchical) as y in fit if self.get_tag(“X-y-must-have-same-index”), must contain fh.index
- marginalbool, optional (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 sktime compatible format, optional (default=None)
Exogeneous time series to fit to
Should be of same scitype (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, 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
- Xpd.DataFrame, or 2D np.ndarray, optional (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 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”.
- 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 sktime compatible format, optional (default=None)
Exogeneous time series to fit to
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 and y.index both
- 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.
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(“scitype:y”)==”univariate”:
must have a single column/variable
- if self.get_tag(“scitype:y”)==”multivariate”:
must have 2 or more columns
if self.get_tag(“scitype:y”)==”both”: no restrictions apply
- 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
sMAPE loss of self.predict(fh, X) with respect to y_test.
- 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_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 settting 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 sktime 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 scitype as in fit: Series scitype: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
for vanilla forecasting, one time series
- Panel scitype: 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 scitype: pd.DataFrame with 3 or more level row MultiIndex
for hierarchical forecasting
- Number of columns admissible depend on the “scitype:y” tag:
- if self.get_tag(“scitype:y”)==”univariate”:
y must have a single column/variable
- if self.get_tag(“scitype:y”)==”multivariate”:
y must have 2 or more columns
if self.get_tag(“scitype:y”)==”both”: no restrictions on columns apply
- For further details:
on usage, see forecasting tutorial examples/01_forecasting.ipynb on specification of formats, examples/AA_datatypes_and_datasets.ipynb
- Xtime series in sktime compatible format, optional (default=None)
Exogeneous time series to fit 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 there are no restrictions on number of columns (unlike for y)
- update_paramsbool, optional (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 sktime 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 scitype as in fit: Series scitype: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
for vanilla forecasting, one time series
- Panel scitype: 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 scitype: pd.DataFrame with 3 or more level row MultiIndex
for hierarchical forecasting
- Number of columns admissible depend on the “scitype:y” tag:
- if self.get_tag(“scitype:y”)==”univariate”:
y must have a single column/variable
- if self.get_tag(“scitype:y”)==”multivariate”:
y must have 2 or more columns
if self.get_tag(“scitype:y”)==”both”: no restrictions on columns apply
- For further details:
on usage, see forecasting tutorial examples/01_forecasting.ipynb on specification of formats, examples/AA_datatypes_and_datasets.ipynb
- 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 sktime compatible format, optional (default=None)
Exogeneous time series for updating and forecasting 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 and fh.index both
there are no restrictions on number of columns (unlike for y)
- update_paramsbool, optional (default=True)
whether model parameters should be updated in each update step
- 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
- 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 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 sktime 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 scitype as in fit: Series scitype: pd.Series, pd.DataFrame, or np.ndarray (1D or 2D)
for vanilla forecasting, one time series
- Panel scitype: 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 scitype: pd.DataFrame with 3 or more level row MultiIndex
for hierarchical forecasting
- Number of columns admissible depend on the “scitype:y” tag:
- if self.get_tag(“scitype:y”)==”univariate”:
y must have a single column/variable
- if self.get_tag(“scitype:y”)==”multivariate”:
y must have 2 or more columns
if self.get_tag(“scitype:y”)==”both”: no restrictions on columns apply
- For further details:
on usage, see forecasting tutorial examples/01_forecasting.ipynb on specification of formats, examples/AA_datatypes_and_datasets.ipynb
- fhint, list, np.array or ForecastingHorizon, optional (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 sktime compatible format, optional (default=None)
Exogeneous time series for updating and forecasting
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 and fh.index both
- update_paramsbool, optional (default=False)
- Returns
- y_predtime series in sktime 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 scitype, same format (see above)