outlier_detection

outlier_detection

OutlierDetectionEvents

OutlierDetectionEvents(
    dataframe: DataFrame,
    value_column: str,
    event_uuid: str = "outlier_event",
    time_threshold: str = "5min",
)

Bases: Base

Processes time series data to detect outliers based on specified statistical methods.

Initializes the OutlierDetectionEvents with specific attributes for outlier detection.

Parameters:

Name	Type	Description	Default
dataframe	DataFrame	The input time series DataFrame.	required
value_column	str	The name of the column containing the values for outlier detection.	required
event_uuid	str	A UUID or identifier for detected outlier events.	'outlier_event'
time_threshold	str	The time threshold to group close events together.	'5min'

detect_outliers_zscore

detect_outliers_zscore(
    threshold: float = 3.0, include_singles: bool = True
) -> pd.DataFrame

Detects outliers using the Z-score method.

Parameters:

Name	Type	Description	Default
threshold	float	The Z-score threshold for detecting outliers.	3.0
include_singles	bool	Whether to include single outliers in the output. Default is True.	True

Returns:

Type	Description
DataFrame	pd.DataFrame: A DataFrame of detected outliers and grouped events.

detect_outliers_iqr

detect_outliers_iqr(
    threshold: tuple = (1.5, 1.5),
    include_singles: bool = True,
) -> pd.DataFrame

Detects outliers using the IQR method.

Parameters:

Name	Type	Description	Default
threshold	tuple	The multipliers for the IQR range for detecting outliers (lower, upper).	(1.5, 1.5)
include_singles	bool	Whether to include single outliers in the output. Default is True.	True

Returns:

Type	Description
DataFrame	pd.DataFrame: A DataFrame of detected outliers and grouped events.

detect_outliers_mad

detect_outliers_mad(
    threshold: float = 3.5, include_singles: bool = True
) -> pd.DataFrame

Detects outliers using the Median Absolute Deviation (MAD) method. This method is more robust to outliers than z-score.

Parameters:

Name	Type	Description	Default
threshold	float	The MAD threshold for detecting outliers. Default is 3.5.	3.5
include_singles	bool	Whether to include single outliers in the output. Default is True.	True

Returns:

Type	Description
DataFrame	pd.DataFrame: A DataFrame of detected outliers and grouped events.

detect_outliers_isolation_forest

detect_outliers_isolation_forest(
    contamination: float = 0.1,
    include_singles: bool = True,
    random_state: Optional[int] = 42,
) -> pd.DataFrame

Detects outliers using sklearn's IsolationForest algorithm. Falls back gracefully if sklearn is not available.

Parameters:

Name	Type	Description	Default
contamination	float	The proportion of outliers in the dataset. Default is 0.1.	0.1
include_singles	bool	Whether to include single outliers in the output. Default is True.	True
random_state	Optional[int]	Random state for reproducibility. Default is 42.	42

Returns:

Type	Description
DataFrame	pd.DataFrame: A DataFrame of detected outliers and grouped events.

Raises:

Type	Description
ImportError	If sklearn is not installed.