pysad.models.SeasonalHybridESD
- class pysad.models.SeasonalHybridESD(period, window_size, max_anomalies, alpha=0.05, robust=True, **stl_kwargs)[source]
Window-based Seasonal Hybrid ESD model [BHVK17].
This is the paper’s S-H-ESD method: use the same modified STL residual as
SeasonalESD, then replace ESD’s mean and standard deviation with median and MAD-based scale in the test statistic.Methods
__init__(period, window_size, max_anomalies)fit(X[, y])Fits the model to all instances in order.
fit_partial(X[, y])Adds the next instance to the model window.
fit_score(X[, y])This helper method applies fit_score_partial to all instances in order.
fit_score_partial(X[, y])Adds and scores the next instance without adding it twice.
score(X)Scores all instances via score_partial iteratively.
Scores whether the next instance is anomalous in a candidate window.
- fit(X, y=None)
Fits the model to all instances in order.
- fit_partial(X, y=None)
Adds the next instance to the model window.
- fit_score(X, y=None)
This helper method applies fit_score_partial to all instances in order.
- Parameters:
X (np.float64 array of shape (num_instances, num_features)) – The instances in order to fit.
y (np.int32 array of shape (num_instances, )) – The labels of the instances in order to fit (Optional for unsupervised models, default=None).
- Returns:
The anomalousness scores of the instances in order.
- Return type:
np.float64 array of shape (num_instances,)
- fit_score_partial(X, y=None)
Adds and scores the next instance without adding it twice.
- score(X)
Scores all instances via score_partial iteratively.
- Parameters:
X (np.float64 array of shape (num_instances, num_features)) – The instances in order to score.
- Returns:
The anomalousness scores of the instances in order.
- Return type:
np.float64 array of shape (num_instances,)
- score_partial(X)
Scores whether the next instance is anomalous in a candidate window.