Once your model is trained, you must deploy the model on Abacus.AI platform to generate predictions. You can use the prediction dashboard to generate the predictions from the trained model. In this section the underlying prediction API and all other additional prediction API methods are discussed for the use case in consideration:

Method

getTimeseriesAnomalies POST

Copy POST

Returns a list of anomalous timestamps from the training dataset.

Arguments:

REQUIRED	KEY	TYPE	DESCRIPTION
Yes	deploymentToken	str	The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
Yes	deploymentId	str	The unique identifier to a deployment created under the project.
No	startTimestamp	str	timestamp from which anomalies have to be detected in the training data
No	endTimestamp	str	timestamp to which anomalies have to be detected in the training data
No	queryData	Union[dict, str, list]	additional data on which anomaly detection has to be performed, it can either be a single record or list of records or a json string representing list of records
No	getAllItemData	bool	set this to true if anomaly detection has to be performed on all the data related to input ids
No	seriesIds	list[str]	list of series ids on which the anomaly detection has to be performed

Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.

Response:

KEY	TYPE	DESCRIPTION
success	Boolean	true if the call succeeded, false if there was an error
	None

Exceptions:

TYPE	WHEN
DataNotFoundError	`deploymentId` is not found.
DataNotFoundError	`seriesIds` is not found.

Language:

Predict API

Arguments:

Response:

Exceptions: