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async jobs

Submit an async anomaly_detection job

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Queues a anomaly_detection job and returns immediately with its job_id. The job runs in a sandbox; poll GET /v2/anomaly_detection/jobs/{job_id} for its state and result. Accepts the same body as the synchronous endpoint plus an optional job_options.

post/v2/anomaly_detection/async

Request

  • Base URL: https://api.nixtla.io
  • URL: https://api.nixtla.io/v2/anomaly_detection/async
  • Auth: HTTP bearer

Request body

freqstring 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_sizeinteger 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 fewer than 6 * detection_size forecasted samples.

threshold_method'univariate' | 'multivariate'

The thresholding method to detect anomalies

hinteger required

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

modelstring

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 support@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_firstboolean

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.

finetune_stepsinteger

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.

finetune_loss'default' | 'mae' | 'mse' | 'rmse' | 'mape' | 'smape' | 'poisson'

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

finetune_depth1 | 2 | 3 | 4 | 5

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. Note that this parameter is only effective for timegpt-1 and timegpt-1-long-horizon models; it has no effect on the other models. By default, the value is set to 1.

finetuned_model_idstring nullable

ID of previously finetuned model

step_sizeinteger nullable

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

hist_exoginteger[] nullable

Zero-based indices of the exogenous features to treat as historical.

refitboolean

Fine-tune the model in each window. If False, only fine-tunes on the first window. Only used if finetune_steps > 0.

multivariateboolean

Compute multivariate predictions across a batch of multiple time series. Requires all time series with overlapping dates. Note that this is only effective for timegpt-2.1 model and it has no effect on the other models. By default, the value is set to False.

model_parametersobject nullable

Optional dictionary of parameters to customize the behavior of the large time model.

Response

Successful Response

job_idstring required

Identifier for the accepted job. Prefixed per task (e.g. fc- for forecast).

Example response

{
  "job_id": "fc-4f2a1c9e8b7d4a6f9c3e1b5d7a9f2c4e"
}

Changes