Check Anomaly
This model can verify whether a data point is an anomaly, for a specific data point when the data is provided through Qualetics data streaming. Data ingestion through Qualetics API is required for this model to return results.
Note: Data Streaming integration is required for this model. Please complete the integration setup, verify the data connection and ensure that the streaming is Live before using this model.
Name | Type | Required | Pre-Defined Values | Info |
|---|---|---|---|---|
fieldname | Text | Yes |  | Name of the field on which we need to detect the anomalous values. |
value | Number | Yes |  | Provide a specific value to check whether it is an anomaly |
timestamp | DateTime | Yes |  | Provide the timestamp corresponding to the value in YYYY-MM-DD HH:MM:SS format |
threshold | Number | Yes | 0 - 100 | What percentage of deviation from the mean should we consider as an anomaly? |
smoothness | Text |  | low, normal, high | Smoothing or alpha value - do you want to smoothen the spikes and dips in the data? If not provided, "normal" is applied as the default value |
duration | Number |  |  | Duration in number of days to fetch data for detecting anomalies. If not provided, a value of 1 is applied as the default value |
actor | Text |  |  | If looking for anomalies in a specific user's data, provide the user id. This value needs to match the Actor id included in the Actor data object. If not provided, all user data is considered. |
action | Text |  |  | If looking for anomalies for a specific type of event, provide the action or event name. This value needs to match the Action names included in the Action data object. If not provided, all event data is considered. |
context | Text |  |  | If looking for anomalies for a specific context, provide the context name. This value needs to match the Context names included in the Contextdata object. If not provided, all event data is considered. |
Rest API Input Example
The following Rest API code is used to verify whether a particular event is an anomaly based on the values recorded on a temperature sensor data stream, and specifically checking whether deviation from the mean is greater than or equal to 40%.
Parameter Name | Parameter Type | Info |
|---|---|---|
expected value | Number |  |
deviation from expected | Number |  |
value | Number |  |
timestamp | DateTime |  |
id | Number | Unique ID of the Event |
Rest API Output Example
Every model execution output consists of the following standard output parameters
- input
- The input string required for the model to extract the categories
- original input
- This is the input provided to the first step in model which is retained across multiple steps in a Data Machine workflow.
- final result
- The result of the model executed in the final step of the Data Machine workflow
- sessionid
- A unique session id that is generated for every execution of a Data Machine which can be used to retain results across multiple sessions
- status
- The result of the Data Machine execution. If all of the steps in a sequence are successfully executed, a value of "Completed" is provided. If the execution is interrupted at any point, a value of "Terminated" is provided with the reason for Termination.

