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