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sktime

Croston

Croston

class Croston(smoothing=0.1)[source]

Croston’s method for forecasting intermittent time series.

Implements the method proposed by Croston in [1] and described in [2].

Croston’s method is a modification of (vanilla) exponential smoothing to handle intermittent time series. A time series is considered intermittent if many of its values are zero and the gaps between non-zero entries are not periodic.

Croston’s method will predict a constant value for all future times, so Croston’s method essentially provides another notion for the average value of a time series.

The method is (equivalent to) the following:

  • Let \(v_0,\ldots,v_n\) be the non-zero values of the time series

  • Let \(v\) be the exponentially smoothed average of \(v_0,\ldots,v_n\)

  • Let \(z_0,\ldots,z_n\) be the number of consecutive zeros plus 1 between the \(v_i\) in the original time series.

  • Let \(z\) be the exponentially smoothed average of \(z_0,\ldots,z_n\)

  • Then the forecast is \(\frac{v}{z}\)

The intuition is that \(v\) is a weighted average of the non-zero time series values and \(\frac{1}{z}\) estimates the probability of getting a non-zero value.

Example to illustrate the \(v\) and \(z\) notation.

  • If the original time series is \(0,0,2,7,0,0,0,-5\) then:

    • The \(v\)’s are \(2,7,-5\)

    • The \(z\)’s are \(3,1,4\)

Parameters:
smoothingfloat, default = 0.1

Smoothing parameter in exponential smoothing

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.

See also

ExponentialSmoothing

References

[1]

J. D. Croston. Forecasting and stock control for intermittent demands. Operational Research Quarterly (1970-1977), 23(3):pp. 289-303, 1972.

[2]

  1. Vandeput. Forecasting Intermittent Demand with the Croston Model.

https://towardsdatascience.com/croston-forecast-model-for-intermittent-demand-360287a17f5f

Examples

>>> from sktime.forecasting.croston import Croston
>>> from sktime.datasets import load_PBS_dataset
>>> y = load_PBS_dataset()
>>> forecaster = Croston(smoothing=0.1)
>>> forecaster.fit(y)
Croston(...)
>>> y_pred = forecaster.predict(fh=[1,2,3])

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([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.

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.