outlierByArima

systemds.operator.algorithm.outlierByArima(X: Matrix, **kwargs: Dict[str, DAGNode | str | int | float | bool])

Built-in function for detecting and repairing outliers in time series, by training an ARIMA model and classifying values that are more than k standard-deviations away from the predicated values as outliers.

Parameters:

• X – Matrix X

• k – threshold values 1, 2, 3 for 68%, 95%, 99.7% respectively (3-sigma rule)

• repairMethod – values: 0 = delete rows having outliers, 1 = replace outliers as zeros 2 = replace outliers as missing values

• p – non-seasonal AR order

• d – non-seasonal differencing order

• q – non-seasonal MA order

• P – seasonal AR order

• D – seasonal differencing order

• Q – seasonal MA order

• s – period in terms of number of time-steps

• include_mean – If the mean should be included

• solver – solver, is either “cg” or “jacobi”

Returns:

Matrix X with no outliers