NInixtla

Foundational Time Series Model Multi Series Anomaly Detector

Changed on

Based on the provided data, this endpoint detects the anomalies in the historical period of multiple time series at once. It takes a JSON as an input containing information like the series frequency and historical data. (See below for a full description of the parameters.) The response contains a flag indicating if the date has an anomaly and also provides the prediction interval used to define if an observation is an anomaly.Get your token at https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.

post/v2/anomaly_detection

Request

  • Base URL
    https://api.nixtla.io
  • 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.

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.

finetuned_model_idstring nullable

ID of previously finetuned model

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.

hist_exoginteger[] nullable

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

Response

Successful Response

input_tokensinteger required
output_tokensinteger required
finetune_tokensinteger required
meannumber[] required
sizesinteger[] required
intervalsobject nullable
weights_xnumber[] nullable
anomalyboolean[] required

Changes

  • 015ac23a238aRevision changes
    • info

      added the new optional request property /

  • a3b589142ebaRevision changes
    • warning

      removed the request property

    • warning

      removed the optional property from the response with the status