Streaming Peaks-Over-Threshold (SPOT)#

class oats.threshold.spot.SPOTThreshold(q: float = 0.0001, level: float = 0.95, memory: int = 2000, support: float = 0, init_cutoff: float = 1, robust: bool = False, estimator: str = 'MoM', **kwargs)[source]#

Bases: Threshold

Streaming version of POT Fit the tails of the data with Generalized Pareto Distribution (GPD). Find the threshold where P(thres) < q. Usual values for q is 1e-3 to 1e-6.

fit() is required to compute the initial threshold for the first n points.

Siffer, Alban and Fouque, Pierre-Alain and Termier, Alexandre and Largouet, Christine “Anomaly Detection in Streams with Extreme Value Theory” https://doi.org/10.1145/3097983.3098144

Parameters:

  • q (float, optional) – q level such that P(threshold) < q. Defaults to 1e-4.

  • level (float, optional) – threshold to fit tail distribution. Defaults to 0.95.

  • memory (int, optional) – how many data points for the tails. Defaults to 2000.

  • support (float, optional) – used for method of moments estimation to prevent a bad fit (MoM is fast but unreliable); threshold is computed as the maximum of GPD and support * HalfNormal. Defaults to 0.

  • init_cutoff (float, optional) – used for method of moments estimation to prevent a bad fit (MoM is not robust against outliers); only bottom init_cutoff deciles are used to fit the GPD distribution. Defaults to 1.

  • robust (bool, optional) – (experimental) using robust estimates of mean and variance to fit GPD using MoM. Defaults to False.

  • estimator (str, optional) – estimator used to fit GPD distribution; accepts ‘MoM’ or ‘MLE’. Defaults to “MoM”.

property adjusted_n#
fit(data)[source]#
get_threshold(data)[source]#
property n_peaks#