> ## Documentation Index
> Fetch the complete documentation index at: https://docs.startree.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# AnomalyDetector

`AnomalyDetector` operators take one or more inputs, and detect anomalies.

There are many types of anomaly detectors. The type is given in the node parameters.

```json theme={null}
 {
  "name": "root",              # name of the operator
  "type": "AnomalyDetector",   # type of operator
  "params": {
    "type": "THRESHOLD",       # type of anomaly detector,
    # other parameters ...
    "component.timestamp": "ts",
    "component.metric": "metric",
    ...
  },
  "inputs" : [...],
  "outputs" : [...]
}
```

## Shared parameters

Anomaly detectors share a set of parameters. Detector-type specific parameters are described in the sub-sections.

| name                              | description                                                                                                                                             | default value |
| --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------- |
| `component.timestamp`             | The name of the time index column.                                                                                                                      | `timestamp`   |
| `component.metric`                | The name of the metric column to analyse.                                                                                                               | `metric`      |
| `component.monitoringGranularity` | The granularity of the data used for detection. Mandatory.   Used to compute the end time of an anomaly if it is the last data point of the input data. |               |

## Commonly-used parameters

These parameters are used by many detectors.

### `component.pattern`

Detect as an anomaly if the metric drop, rise or both directions. `UP`, `DOWN`, `UP_OR_DOWN`.

| name         | description                                                              |
| ------------ | ------------------------------------------------------------------------ |
| `UP`         | Detect an anomaly only if the current time series is above the baseline. |
| `DOWN`       | Detect an anomaly only if the current time series is below the baseline. |
| `UP_OR_DOWN` | Detect an anomaly in both directions.                                    |