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POST
/
v2
/
anomaly_detection
Foundational Time Series Model Multi Series Anomaly Detector
curl --request POST \
  --url https://api.nixtla.io/v2/anomaly_detection \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "series": {
    "sizes": [
      35
    ],
    "y": [
      0,
      1,
      2,
      3,
      4,
      5,
      6,
      0,
      1,
      2,
      3,
      4,
      5,
      6,
      0,
      1,
      2,
      3,
      4,
      5,
      6,
      0,
      1,
      2,
      3,
      4,
      5,
      6,
      0,
      1,
      2,
      10,
      4,
      5,
      6
    ]
  },
  "freq": "D",
  "level": 90
}'
{
  "input_tokens": 1,
  "output_tokens": 1,
  "finetune_tokens": 1,
  "mean": [
    123
  ],
  "sizes": [
    123
  ],
  "intervals": {},
  "weights_x": [
    123
  ],
  "feature_contributions": [
    [
      123
    ]
  ],
  "anomaly": [
    true
  ]
}

Authorizations

​
Authorization
string
header
required

HTTPBearer

Body

application/json
​
series
object
required
​
freq
string
required

The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available.

​
model
any

Model to use as a string. Common options are (but not restricted to) timegpt-1 and timegpt-1-long-horizon. Full options vary by different users. Contact ops@nixtla.io for more information. We recommend using timegpt-1-long-horizon for forecasting if you want to predict more than one seasonal period given the frequency of your data.

​
clean_ex_first
boolean
default:true

A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model.

​
finetuned_model_id
string | null

ID of previously finetuned model

​
level
default:99

Specifies the confidence level for the prediction interval used in anomaly detection. It is represented as a percentage between 0 and 100. For instance, a level of 95 indicates that the generated prediction interval captures the true future observation 95% of the time. Any observed values outside of this interval would be considered anomalies. A higher level leads to wider prediction intervals and potentially fewer detected anomalies, whereas a lower level results in narrower intervals and potentially more detected anomalies. Default: 99.

Required range: 0 <= x < 100

Response

Successful Response

​
input_tokens
integer
required
Required range: x >= 0
​
output_tokens
integer
required
Required range: x >= 0
​
finetune_tokens
integer
required
Required range: x >= 0
​
mean
number[]
required
​
sizes
integer[]
required
​
anomaly
boolean[]
required
​
intervals
object | null
​
weights_x
number[] | null
​
feature_contributions
number[][] | null
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