Advanced Use Cases

The Science of Anomaly Detection

Anomaly Detection comes in many forms. We will go through some of these and give guidance as to whether this Operator is going to be helpful for each use case.

• Constructive v Destructive v Pre-Processing: This Operator focuses on the Constructive and Pre-Processing use cases. Destructive can work, but more specific parameters may be required.

• Supervised v Semi-Supervised v Unsupervised: All 3 of these approaches are supported by AutoMLX. AutoTS supports only Unsupervised at this time.

• Time Series. This Operator requires time-series data.

Data Parameterization

Read Data from the Database

kind: operator
type: anomaly
version: v1
spec:
    input_data:
        connect_args:
            user: XXX
            password: YYY
            dsn: "localhost/orclpdb"
        sql: 'SELECT Store_ID, Sales, Date FROM live_data'
    datetime_column:
        name: ds
    target_column: y

Read Part of a Dataset

kind: operator
type: anomaly
version: v1
spec:
    input_data:
        url: oci://bucket@namespace/data
        format: hdf
        limit: 1000  # Only the first 1000 rows
        columns: ["y", "ds"]  # Ignore other columns
    datetime_column:
        name: ds
    target_column: y

Model Parameterization

Specify Model Type

Sometimes users will know which models they want to use. When users know this in advance, they can specify using the model_kwargs dictionary. In the following example, we will instruct the model to only use the IsolationForestOD model.

kind: operator
type: anomaly
version: v1
spec:
  model: automlx
  model_kwargs:
    model_list:
      - IsolationForestOD
    search_space:
      IsolationForestOD:
        n_estimators:
          range': [10, 50]
          type': 'discrete'

AutoTS offers the same extensibility:

kind: operator
type: anomaly
version: v1
spec:
  model: autots
  model_kwargs:
    method: IQR