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.

  • The operator currently supports only unsupervised learning and works with both time-series and non-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