UpdateRefitsEvery

class UpdateRefitsEvery(forecaster, refit_interval=0, refit_window_size=None, refit_window_lag=0)[source]

Refits periodically when update is called.

If update is called with update_params=True and refit_interval or more has elapsed since the last fit, refits the forecaster instead (call to fit).

Refitting is done on (potentially) all data seen so far.

refit_window controls the lookback window on which refitting is done. The refit is carried out on all data in the lookback window cutoff (inclusive, end) to cutoff minus refit_window (exclusive, start), with a default lookback window of all data seen so far.

Parameters:

forecasteran sktime forecaster

the forecaster to be refit/updated regularly

refit_intervaldifference of sktime time indices (int or timedelta), optional

interval that needs to elapse after which the first update defaults to fit default = 0, i.e., always refits, never updates

if index of y seen in fit is integer or y is index-free container type, refit_interval must be int, and is interpreted as difference of int location
if index of y seen in fit is timestamp, must be int or pd.Timedelta
- if pd.Timedelta, will be interpreted as time since last refit elapsed
- if int, will be interpreted as number of time stamps seen since last refit

refit_window_sizedifference of sktime time indices (int or timedelta), optional

length of the data window to refit to in case update calls fit; default = inf, i.e., refits to entire training data seen so far

refit_window_lagdifference of sktime indices (int or timedelta), optional

lag of the data window to refit to, w.r.t. cutoff, in case update calls fit; default = 0, i.e., refit window ends with and includes cutoff

Attributes:

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

Examples

>>> from sktime.forecasting.trend import TrendForecaster
>>> from sktime.forecasting.stream import UpdateRefitsEvery
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> y0 = y.iloc[:-20]
>>> y1 = y.iloc[-20:-10]
>>> y2 = y.iloc[-10:]
>>> forecaster = TrendForecaster()
>>> forecaster = UpdateRefitsEvery(forecaster, refit_interval=12)
>>> forecaster.fit(y0, fh=[1,2,3])
UpdateRefitsEvery(...)
>>> # predict etc could be called here
>>> # e.g., forecaster.predict()
>>>
>>> # first update, 10 < refit_interval = 12, so calls update
>>> forecaster.update(y1)
UpdateRefitsEvery(...)
>>> # second update, 20 >= refit_interval = 12, so calls fit
>>> forecaster.update(y2)
UpdateRefitsEvery(...)

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from 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_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`()	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.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.