---
title: "Foundational Time Series Model Multi Series Anomaly Detector"
method: POST
path: "/v2/anomaly_detection"
---

# Foundational Time Series Model Multi Series Anomaly Detector

`POST /v2/anomaly_detection`

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://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.

## Request body

- AnomalyDetectionInput
  - `series` SeriesWithFutureExogenous, required
    - `X_future` array[], nullable — Future values of the exogenous features. Each feature must be a list of size number of series times the forecast horizon (h).
      - union[]
        - union
          - number
          - string
    - `X` array[], nullable — Historic values of the exogenous features. Each feature must be a list of the same size as the target (y).
      - union[]
        - union
          - number
          - string
    - `categorical_exog` integer[], nullable — Zero-based indices of the columns in X that are categorical features.
    - `y` number[], required — Historic values of the target.
    - `sizes` integer[], required — Sizes of the individual series.
  - `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` string — 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_first` boolean — 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, nullable — ID of previously finetuned model
  - `multivariate` boolean — 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_parameters` object, nullable — Optional dictionary of parameters to customize the behavior of the large time model.
  - `hist_exog` integer[], nullable — Zero-based indices of the exogenous features to treat as historical.
  - `level` union — 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.
    - integer
    - number

## Response `200`

Successful Response

- AnomalyDetectionOutput
  - `input_tokens` integer, required
  - `output_tokens` integer, required
  - `finetune_tokens` integer, required
  - `mean` number[], required
  - `sizes` integer[], required
  - `intervals` object, nullable
  - `weights_x` number[], nullable
  - `anomaly` boolean[], required

## Other responses

- `422` — Validation Error

---

[API](https://skmtc.net/nixtla/apis/nixtla-forecast-api.md) · [All operations](https://skmtc.net/nixtla/apis/nixtla-forecast-api/llms.txt) · [OpenAPI document](https://skmtc-service-staging.skmtc.workers.dev/v1/apis/nixtla/nixtla-forecast-api/revisions/a3b589142eba/schema)