RollingStandardDeviationDetector

RollingStandardDeviationDetector(window_size=10, max_std=np.inf, center=True)

Detect large variations.

Parameters

Name	Type	Description	Default
window_size	int	Number of data points to evaluate over.	10
max_std	float	Maximum standard deviation to accept as normal.	np.inf
center	bool	If True, set the labels at the center of the window.	True

Methods

Name	Description
detect	Detect anomalies
fit	Set detector parameters based on data.
save	Save a detector for later use.
validate	Check that input data is in correct format and possibly adjust.

detect

RollingStandardDeviationDetector.detect(data)

Detect anomalies

Parameters

Name	Type	Description	Default
data	Union[pd.Series, pd.DataFrame]	Time series data with possible anomalies	required

Returns

Name	Type	Description
	pd.Series or pd.DataFrame	Time series with bools, True == anomaly.

fit

RollingStandardDeviationDetector.fit(data)

Set detector parameters based on data.

Parameters

Name	Type	Description	Default
data	Union[pd.Series, pd.DataFrame]	Normal (non-anomalous) time series data for training. If DataFrame, must contain exactly one column	required

Returns

Name	Type	Description
	Self

save

RollingStandardDeviationDetector.save(path)

Save a detector for later use.

Parameters

Name	Type	Description	Default
path	str or Path	File path to save the detector to.	required

validate

RollingStandardDeviationDetector.validate(data)

Check that input data is in correct format and possibly adjust.

Parameters

Name	Type	Description	Default
data	pd.Series or pd.DataFrame	Input data to validate.	required

Returns

Name	Type	Description
	pd.DataFrame	Validated data.

Raises

Name	Type	Description
	WrongInputDataTypeError	If data is not a pd.Series or pd.DataFrame.