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

# Detect Anomalies (Entire Series)

> Azure-compatible endpoint to analyze an entire time series for anomalies

Detects anomalies across all points in a time series. This endpoint is fully compatible with the Azure Anomaly Detector API (v1.0 and v1.1). Best for batch analysis of historical data.

Both `/anomalydetector/v1.0/timeseries/entire/detect` and `/anomalydetector/v1.1/timeseries/entire/detect` are supported. Canary returns the `severity` field for both versions.

<ParamField body="series" type="array" required>
  Array of data points, each with `timestamp` (ISO 8601) and `value` (number). Minimum 12 points, maximum 8640 points. Timestamps must be sorted ascending with no duplicates.
</ParamField>

<ParamField body="granularity" type="string" default="none">
  Time interval between points. One of: `yearly`, `monthly`, `weekly`, `daily`, `hourly`, `minutely`, `secondly`, `microsecond`, `none`.
</ParamField>

<ParamField body="customInterval" type="integer" default={1}>
  Multiplier for granularity.
</ParamField>

<ParamField body="period" type="integer" default={0}>
  Seasonality period. Set to `0` for auto-detection, or provide a positive integer to force a specific period.
</ParamField>

<ParamField body="maxAnomalyRatio" type="number" default={0.25}>
  Maximum fraction of points that can be flagged as anomalies. Must be between 0 and 0.5 (exclusive).
</ParamField>

<ParamField body="sensitivity" type="integer" default={95}>
  Detection sensitivity from 0 (least sensitive) to 99 (most sensitive). Higher values flag more anomalies.
</ParamField>

<ResponseField name="period" type="integer">
  Detected seasonality period (0 if none detected).
</ResponseField>

<ResponseField name="expectedValues" type="number[]">
  The model's expected value for each point.
</ResponseField>

<ResponseField name="upperMargins" type="number[]">
  Upper bound margin for each point.
</ResponseField>

<ResponseField name="lowerMargins" type="number[]">
  Lower bound margin for each point.
</ResponseField>

<ResponseField name="isAnomaly" type="boolean[]">
  Whether each point is an anomaly.
</ResponseField>

<ResponseField name="isNegativeAnomaly" type="boolean[]">
  Whether each point is a negative (below expected) anomaly.
</ResponseField>

<ResponseField name="isPositiveAnomaly" type="boolean[]">
  Whether each point is a positive (above expected) anomaly.
</ResponseField>

<ResponseField name="severity" type="number[]">
  Severity score from 0.0 to 1.0 for each point.
</ResponseField>

<RequestExample>
  ```bash cURL theme={null}
  curl -X POST https://api.canaryedge.com/anomalydetector/v1.1/timeseries/entire/detect \
    -H "Content-Type: application/json" \
    -H "Ocp-Apim-Subscription-Key: YOUR_API_KEY" \
    -d '{
      "series": [
        {"timestamp": "2018-03-01T00:00:00Z", "value": 32858923},
        {"timestamp": "2018-03-02T00:00:00Z", "value": 29615278},
        {"timestamp": "2018-03-03T00:00:00Z", "value": 28988966},
        {"timestamp": "2018-03-04T00:00:00Z", "value": 28943783},
        {"timestamp": "2018-03-05T00:00:00Z", "value": 33521547},
        {"timestamp": "2018-03-06T00:00:00Z", "value": 34127843},
        {"timestamp": "2018-03-07T00:00:00Z", "value": 32497298},
        {"timestamp": "2018-03-08T00:00:00Z", "value": 29615278},
        {"timestamp": "2018-03-09T00:00:00Z", "value": 28988966},
        {"timestamp": "2018-03-10T00:00:00Z", "value": 28943783},
        {"timestamp": "2018-03-11T00:00:00Z", "value": 33521547},
        {"timestamp": "2018-03-12T00:00:00Z", "value": 72000000}
      ],
      "granularity": "daily",
      "sensitivity": 95
    }'
  ```

  ```python Python theme={null}
  import requests

  response = requests.post(
      "https://api.canaryedge.com/anomalydetector/v1.1/timeseries/entire/detect",
      headers={
          "Content-Type": "application/json",
          "Ocp-Apim-Subscription-Key": "YOUR_API_KEY",
      },
      json={
          "series": [
              {"timestamp": "2018-03-01T00:00:00Z", "value": 32858923},
              {"timestamp": "2018-03-02T00:00:00Z", "value": 29615278},
              {"timestamp": "2018-03-03T00:00:00Z", "value": 28988966},
              {"timestamp": "2018-03-04T00:00:00Z", "value": 28943783},
              {"timestamp": "2018-03-05T00:00:00Z", "value": 33521547},
              {"timestamp": "2018-03-06T00:00:00Z", "value": 34127843},
              {"timestamp": "2018-03-07T00:00:00Z", "value": 32497298},
              {"timestamp": "2018-03-08T00:00:00Z", "value": 29615278},
              {"timestamp": "2018-03-09T00:00:00Z", "value": 28988966},
              {"timestamp": "2018-03-10T00:00:00Z", "value": 28943783},
              {"timestamp": "2018-03-11T00:00:00Z", "value": 33521547},
              {"timestamp": "2018-03-12T00:00:00Z", "value": 72000000},
          ],
          "granularity": "daily",
          "sensitivity": 95,
      },
  )

  print(response.json())
  ```
</RequestExample>

<ResponseExample>
  ```json 200 theme={null}
  {
    "period": 7,
    "expectedValues": [32889953.0, 29604016.0, 28988966.0],
    "upperMargins": [1644497.6, 1480200.8, 1449448.3],
    "lowerMargins": [1644497.6, 1480200.8, 1449448.3],
    "isAnomaly": [false, false, false, false, false, false, false, false, false, false, false, true],
    "isNegativeAnomaly": [false, false, false, false, false, false, false, false, false, false, false, false],
    "isPositiveAnomaly": [false, false, false, false, false, false, false, false, false, false, false, true],
    "severity": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.72]
  }
  ```
</ResponseExample>