Evaluation status. Contains an error message if the anomalyScore is < 0.
Possible error messages:
"Time series too sparse": The returned time series for this slice did not contain enough data points (we require a minimum of 10).
"Not enough recent time series points": The time series contains the minimum of 10 points, but there are not enough close in time to the detection point.
"Missing detection point data": There were not events to be aggregated within the [detectionTime, detectionTime + granularity] time interval, so we don't have an actual value with which we can compare our prediction.
"Data retrieval error": We failed to retrieve the time series data for this slice and could not evaluate it successfully. Should be a transient error.
"Internal server error": Internal unexpected error.
NOTE: This value can be an estimate, so it should not be used as a source of truth.
The expected value at the detection time, which is obtained by forecasting on the historical time series.
How much our forecast model expects the detection point actual will deviate from its forecasted value based on how well it fit the input time series.
In general, we expect the detectionPointActual to be in the [detectionPointForecast - expectedDeviation,
detectionPointForecast + expectedDeviation] range. The more the actual value is outside this range, the more statistically significant the anomaly is.
The expected deviation is always positive.
Summarizes how significant the change between the actual and forecasted detection points are compared with the historical patterns observed on the history time series.