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TimeseriesAnomalyTrainingConfig

Training config for the TS_ANOMALY problem type

KEY	TYPE	Description
THRESHOLD_SCORE	float	Threshold score for anomaly detection
HYPERPARAMETER_CALCULATION_WITH_HEURISTICS	TimeseriesAnomalyUseHeuristic	Enable heuristic calculation to get hyperparameters for the model
TYPE_OF_SPLIT	TimeseriesAnomalyDataSplitType	Type of data splitting into train/test.
ANOMALY_TYPE	TimeseriesAnomalyTypeOfAnomaly	select what kind of peaks to detect as anomalies
TEST_START	str	Limit training data to dates before the given test start.
TIMESERIES_FREQUENCY	str	set this to control frequency of filling missing values
HANDLE_ZEROS_AS_MISSING_VALUES	bool	If True, handle zero values in numeric columns as missing data
FILL_MISSING_VALUES	List[List[dict]]	strategies to fill missing values and missing timestamps
TEST_SPLIT	int	Percent of dataset to use for test data. We support using a range between 5 ( i.e. 5% ) to 20 ( i.e. 20% ) of your dataset.
MIN_SAMPLES_IN_NORMAL_REGION	int	Adjust this to fine-tune the number of anomalies to be identified.