HampelFilter

class sktime.transformations.series.outlier_detection.HampelFilter(window_length, n_sigma=3, k=1.4826, return_bool=False)[source]

HampelFilter to detect outliers based on a sliding window. Correction of outliers is recommended by means of the sktime.Imputer, so both can be tuned separately.

Parameters
  • window_length (int) – Lenght of the sliding window

  • n_sigma (int, optional) – Defines how strong a point must outly to be an “outlier”, by default 3

  • k (float, optional) – A constant scale factor which is dependent on the distribution, for Gaussian it is approximately 1.4826, by default 1.4826

  • return_bool (bool, optional) – If True, outliers are filled with True and non-outliers with False. Else, outliers are filled with np.nan.

References

Hampel F. R., “The influence curve and its role in robust estimation”, Journal of the American Statistical Association, 69, 382–393, 1974

https://github.com/MichaelisTrofficus/hampel_filter

__init__(window_length, n_sigma=3, k=1.4826, return_bool=False)[source]

Initialize self. See help(type(self)) for accurate signature.