https://azure.com/timeseries/entire/detectThis operation generates a model using an entire series, each point is detected with the same model. With this method, points before and after a certain point are used to determine whether it is an anomaly. The entire detection can give user an overall status of the time series.
{
"success": true,
"data": {
"id": "abc123",
"created_at": "2025-01-01T00:00:00Z"
}
}{
"success": false,
"error": {
"code": "VALIDATION_ERROR",
"message": "Invalid request parameters"
}
}1curl --request POST \2 --url 'https://azure.com/timeseries/entire/detect' \3 --header 'accept: application/json' \4 --header 'content-type: application/json'1{2 "success": true,3 "data": {4 "id": "abc123",5 "created_at": "2025-01-01T00:00:00Z"6 }7}https://azure.com/timeseries/entire/detectThis operation generates a model using an entire series, each point is detected with the same model. With this method, points before and after a certain point are used to determine whether it is an anomaly. The entire detection can give user an overall status of the time series.
{
"success": true,
"data": {
"id": "abc123",
"created_at": "2025-01-01T00:00:00Z"
}
}{
"success": false,
"error": {
"code": "VALIDATION_ERROR",
"message": "Invalid request parameters"
}
}1curl --request POST \2 --url 'https://azure.com/timeseries/entire/detect' \3 --header 'accept: application/json' \4 --header 'content-type: application/json'1{2 "success": true,3 "data": {4 "id": "abc123",5 "created_at": "2025-01-01T00:00:00Z"6 }7}