# density_outliers¶

Functions

 Calculate distance for `get_anomalies_density()` function by taking absolute value of difference. `get_anomalies_density`(ts[, in_column, ...]) Compute outliers according to density rule. Get indices of outliers for one series.
absolute_difference_distance(x: float, y: float) float[source]

Calculate distance for `get_anomalies_density()` function by taking absolute value of difference.

Parameters
• x (float) – first value

• y (float) – second value

Returns

result – absolute difference between values

Return type

float

get_anomalies_density(ts: TSDataset, in_column: str = 'target', window_size: int = 15, distance_coef: float = 3, n_neighbors: int = 3, distance_func: typing.Callable[[float, float], float] = <function absolute_difference_distance>) Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]][source]

Compute outliers according to density rule.

For each element in the series build all the windows of size `window_size` containing this point. If any of the windows contains at least `n_neighbors` that are closer than `distance_coef * std(series)` to target point according to `distance_func` target point is not an outlier.

Parameters
• ts (TSDataset) – TSDataset with timeseries data

• in_column (str) – name of the column in which the anomaly is searching

• window_size (int) – size of windows to build

• distance_coef (float) – factor for standard deviation that forms distance threshold to determine points are close to each other

• n_neighbors (int) – min number of close neighbors of point not to be outlier

• distance_func (Callable[[float, float], float]) – distance function

Returns

dict of outliers in format {segment: [outliers_timestamps]}

Return type

Dict[str, List[pandas._libs.tslibs.timestamps.Timestamp]]

Notes

It is a variation of distance-based (index) outlier detection method adopted for timeseries.