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Detect points where the underlying data distribution changes. Canary implements this by computing the energy gradient: the rate of change of the prediction error over a sliding window. This endpoint is fully compatible with the Azure Anomaly Detector changepoint API. Both /anomalydetector/v1.0/timeseries/changepoint/detect and /anomalydetector/v1.1/timeseries/changepoint/detect are supported.
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series
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.
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granularity
string
default:"none"
Time interval between points. One of: yearly, monthly, weekly, daily, hourly, minutely, secondly, microsecond, none.
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customInterval
integer
default:1
Multiplier for granularity.
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period
integer
default:0
Seasonality period. Set to 0 for auto-detection.
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maxAnomalyRatio
number
Maximum fraction of points flagged. Must be between 0 and 0.5 (exclusive).
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sensitivity
integer
default:95
Detection sensitivity (0-99).
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period
integer
Detected seasonality period.
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isChangePoint
boolean[]
Whether each point is a changepoint.
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confidenceScores
number[]
Confidence score from 0.0 to 1.0 for each point.

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How Changepoint Detection Works

  1. The energy gradient is computed: gradient[i] = mean(energies[i:i+k]) - mean(energies[i-k:i]) where k = max(period, 5).
  2. A point is a changepoint if its gradient exceeds the 95th percentile of all gradients.
  3. The gradient must be a local maximum (greater than or equal to both neighbors).
  4. Minimum spacing between changepoints equals the detected period.
curl -X POST https://api.canaryedge.com/anomalydetector/v1.1/timeseries/changepoint/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
  }'

{
  "period": 7,
  "isChangePoint": [false, false, false, false, false, false, false, false, false, false, false, true],
  "confidenceScores": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.87]
}