Predict API

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.
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