Determines whether the most recent point in a series is anomalous, using prior points as context. This endpoint is fully compatible with the Azure Anomaly Detector API. Ideal for real-time streaming detection where new points arrive one at a time. BothDocumentation Index
Fetch the complete documentation index at: https://docs.canaryedge.com/llms.txt
Use this file to discover all available pages before exploring further.
/anomalydetector/v1.0/timeseries/last/detect and /anomalydetector/v1.1/timeseries/last/detect are supported.
Array of data points. The last point is evaluated; prior points provide context. Minimum 12 points, maximum 8640 points. Timestamps must be sorted ascending with no duplicates.
Time interval between points. One of:
yearly, monthly, weekly, daily, hourly, minutely, secondly, microsecond, none.Multiplier for granularity.
Seasonality period. Set to
0 for auto-detection.Maximum fraction of points that can be flagged as anomalies. Must be between 0 and 0.5 (exclusive).
Detection sensitivity (0-99). Higher values flag more anomalies.
Whether the last point is anomalous.
Whether the last point is a negative anomaly.
Whether the last point is a positive anomaly.
Detected seasonality period.
The model’s predicted value for the last point.
Upper margin for the last point.
Lower margin for the last point.
Recommended context window size for future requests. Calculated as
period * 4 + 1, or max(29, series_length) if no period is detected.Severity score from 0.0 to 1.0.