ConstantGradientDetector

ConstantGradientDetector(window_size=3)

Detect constant gradients.

Typically caused by linear interpolation over a long interval.

Parameters

Name	Type	Description	Default
window_size	int	Minimum window size to consider as anomaly.	3

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

ConstantGradientDetector.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

ConstantGradientDetector.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

ConstantGradientDetector.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

ConstantGradientDetector.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.