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latestOpenAPI 3.1.0raw.githubusercontent.com2026-07-21101576.0 KB

Foundational Time Series Model Multi Series Anomaly Detector

Based on the provided data, this endpoint detects the anomalies in the historical perdiod 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://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.

post/v2/anomaly_detection

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.

feature_contributionsboolean

Compute the exogenous features contributions to the forecast.

Response

Successful Response

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