Transformer

ADICVTransformer

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

Quickstart

python

from sktime.transformations.adi_cv import ADICVTransformer

estimator = ADICVTransformer(features=None, adi_threshold=1.32, cv_threshold=0.49, adi_trim_handling='pool')

Parameters(4)

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

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)

References

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