ADICVTransformer

class ADICVTransformer(features=None, adi_threshold=1.32, cv_threshold=0.49, adi_trim_handling='pool')[source]

Transformer categorizing series into ADI-CV2 classes after Syntetos/Boylan.

Transforms a time series into a category label, which is one of: "smooth", "erratic", "intermittent", or "lumpy".

The labels are based on the Average Demand Interval (ADI) and the Coefficient of Variation squared (CV^2) of the time series, using simple thresholding. Optionally, the transformer can return the ADI and CV^2 values as well.

Let \(x_t\) be the value of the time series at times \(t = 1, 2, \dots, T\), and let \(N\) be the number of non-zero values in the time series, i.e., \(N = \text{card}\{t: x_t \neq 0\}\).

The ADI and CV^2 are calculated as follows:

1. Average Demand Interval (ADI): The average number of periods between periods with non-zero demand, mathematically defined as:

\[ADI = \frac{T}{N}\]

2. Coefficient of Variation squared (CV^2): CV is calculated on non-zero values in the time series. Mathematical definition is:

\[CV2 = \frac{1}{N}\sum_{t=1}^{T} x_t^2 - \left(\frac{1}{N}\sum_{t=1}^{T} x_t\right)^2\]

3. Class: Classification of time series on basis of ADI threshold and CV^2 threshold.

For thresholds ADI_threshold and CV2_threshold, the classes are:

Smooth: If ADI <= adi_threshold and CV2 <= cv2_threshold
Erratic: If ADI <= adi_threshold and CV2 > cv2_threshold
Intermittent: If ADI > adi_threshold and cv2 <= cv2_threshold
Lumpy: if ADI > adi_threshold and CV2 > cv2_threshold

Default values for the thresholds are taken from the paper by Syntetos/Boylan [1]. namely, adi_threshold = 1.32 and cv2_threshold = 0.49. They can also be adjusted by passing them as parameters to the transformer.

Parameters:

adi_thresholdfloat (default = 1.32)

Specifies the ADI threshold utilized for classifying the time series

cv2_thresholdfloat (default = 0.49)

Specifies the CV2 threshold utilized for classifying the time series

featureslist[str] | None (default = [‘adi’, ‘cv2’, ‘class’])

Specifies all of the feature values to be calculated

adi_trim_handling: string (default = pool)

Specifies the method for reconciling leading/trailing zeros in the series. Allowable values are (‘pool’, ‘trim’, and ‘ignore’), corresponding to the following treatment of leading/trailing zeros: pool => L / N trim => (Last N - First N) / N ignore => L / (N - 1)

where L is the set of All Observations, and N is the set of Non-Zero observations

Attributes:

is_fitted: Whether fit has been called.

References

[1]: John E. Boylan, Aris Syntetos: The Accuracy of Intermittent Demand Estimates. International Journal of Forecasting, 1 Apr. 2005

Examples

>>> from sktime.transformations.adi_cv import ADICVTransformer
>>> from sktime.datasets import load_airline
>>> y = load_airline()
>>> transformer = ADICVTransformer()
>>> y_hat = transformer.fit_transform(y)

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`(X[, y])	Fit transformer to X, optionally to y.
`fit_transform`(X[, y])	Fit to data, then transform it.
`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_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.
`inverse_transform`(X[, y])	Inverse transform X and return an inverse transformed version.
`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.
`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.
`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.
`transform`(X[, y])	Transform X and return a transformed version.
`update`(X[, y, update_params])	Update transformer with X, optionally y.