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POST
/
v2
/
online_anomaly_detection
Foundational Time Series Model Online Multi Series Anomaly Detector
curl --request POST \
  --url https://api.nixtla.io/v2/online_anomaly_detection \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
  "series": {
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      320
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  "h": 20,
  "freq": "W",
  "level": 99,
  "detection_size": 5
}'
{
  "input_tokens": 1,
  "output_tokens": 1,
  "finetune_tokens": 1,
  "mean": [
    123
  ],
  "sizes": [
    123
  ],
  "idxs": [
    123
  ],
  "anomaly": [
    true
  ],
  "anomaly_score": [
    123
  ],
  "accumulated_anomaly_score": [
    123
  ],
  "intervals": {}
}

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.

​
detection_size
integer
required

Window over which to detect anomalies starting from the end of the series. This window is not considered when calculating the anomaly threshold to avoid bias from abnormal samples, unless there are less than 6 * detection_size forecasted samples.

Required range: x > 0
​
h
integer
required

The forecasting horizon. This represents the number of time steps into the future that the forecast should predict.

Required range: x > 0
​
threshold_method
enum<string>
default:univariate

The thresholding method to detect anomalies

Available options:
univariate,
multivariate
​
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.

​
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
​
finetune_steps
integer
default:0

The number of tuning steps used to train the large time model on the data. Set this value to 0 for zero-shot inference, i.e., to make predictions without any further model tuning.

Required range: x >= 0
​
finetune_loss
enum<string>
default:default

The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.

Available options:
default,
mae,
mse,
rmse,
mape,
smape,
poisson
​
finetune_depth
enum<integer>
default:1

The depth of the finetuning. Uses a scale from 1 to 5, where 1 means little finetuning, and 5 means that the entire model is finetuned. By default, the value is set to 1.

Available options:
1,
2,
3,
4,
5
​
finetuned_model_id
string | null

ID of previously finetuned model

​
step_size
integer | null

Step size between each cross validation window. If None it will be equal to the forecasting horizon.

Required range: x > 0

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
​
idxs
integer[]
required
​
anomaly
boolean[]
required
​
anomaly_score
number[]
required
​
accumulated_anomaly_score
number[] | null
​
intervals
object | null
⌘I