> ## Documentation Index > Fetch the complete documentation index at: https://nixtla.io/docs/llms.txt > Use this file to discover all available pages before exploring further. # 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 for private beta at https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference. ## OpenAPI ````yaml /openapi.json post /v2/anomaly_detection openapi: 3.1.0 info: title: Nixtla Forecast API description: >- API for TimeGPT forecast. Just send your data as json and get results. We do the heavy lifting. version: 2025.8.3 servers: - url: https://api.nixtla.io security: [] paths: /v2/anomaly_detection: post: summary: Foundational Time Series Model Multi Series Anomaly Detector description: >- 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 for private beta at https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference. operationId: v2_anomaly_detection_v2_anomaly_detection_post requestBody: content: application/json: schema: $ref: '#/components/schemas/AnomalyDetectionInput' examples: - series: sizes: - 35 'y': - 0 - 1 - 2 - 3 - 4 - 5 - 6 - 0 - 1 - 2 - 3 - 4 - 5 - 6 - 0 - 1 - 2 - 3 - 4 - 5 - 6 - 0 - 1 - 2 - 3 - 4 - 5 - 6 - 0 - 1 - 2 - 10 - 4 - 5 - 6 freq: D level: 90 required: true responses: '200': description: Successful Response content: application/json: schema: $ref: '#/components/schemas/AnomalyDetectionOutput' '422': description: Validation Error content: application/json: schema: $ref: '#/components/schemas/HTTPValidationError' security: - HTTPBearer: [] components: schemas: AnomalyDetectionInput: properties: series: $ref: '#/components/schemas/SeriesWithExogenous' freq: type: string title: Freq description: >- The frequency of the data represented as a string. 'D' for daily, 'M' for monthly, 'H' for hourly, and 'W' for weekly frequencies are available. model: title: Model description: >- Model to use as a string. Common options are (but not restricted to) `timegpt-1` and `timegpt-1-long-horizon.` Full options vary by different users. Contact support@nixtla.io for more information. We recommend using `timegpt-1-long-horizon` for forecasting if you want to predict more than one seasonal period given the frequency of your data. default: timegpt-1 clean_ex_first: type: boolean title: Clean Ex First description: >- A boolean flag that indicates whether the API should preprocess (clean) the exogenous signal before applying the large time model. If True, the exogenous signal is cleaned; if False, the exogenous variables are applied after the large time model. default: true finetuned_model_id: anyOf: - type: string pattern: ^[a-zA-Z0-9\-_]{1,36}$ - type: 'null' title: Finetuned Model Id description: ID of previously finetuned model level: anyOf: - type: integer exclusiveMaximum: 100 minimum: 0 - type: number exclusiveMaximum: 100 minimum: 0 title: Level description: >- 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. default: 99 type: object required: - series - freq title: AnomalyDetectionInput AnomalyDetectionOutput: properties: input_tokens: type: integer minimum: 0 title: Input Tokens output_tokens: type: integer minimum: 0 title: Output Tokens finetune_tokens: type: integer minimum: 0 title: Finetune Tokens mean: items: type: number type: array title: Mean sizes: items: type: integer type: array title: Sizes intervals: anyOf: - additionalProperties: items: type: number type: array type: object - type: 'null' title: Intervals weights_x: anyOf: - items: type: number type: array - type: 'null' title: Weights X feature_contributions: anyOf: - items: items: type: number type: array type: array - type: 'null' title: Feature Contributions anomaly: items: type: boolean type: array title: Anomaly type: object required: - input_tokens - output_tokens - finetune_tokens - mean - sizes - anomaly title: AnomalyDetectionOutput HTTPValidationError: properties: detail: items: $ref: '#/components/schemas/ValidationError' type: array title: Detail type: object title: HTTPValidationError SeriesWithExogenous: properties: X: anyOf: - items: items: type: number type: array type: array - type: 'null' title: X description: >- Historic values of the exogenous features. Each feature must be a list of the same size as the target (y). 'y': items: type: number type: array title: 'Y' description: Historic values of the target. sizes: items: type: integer type: array title: Sizes description: Sizes of the individual series. type: object required: - 'y' - sizes title: SeriesWithExogenous ValidationError: properties: loc: items: anyOf: - type: string - type: integer type: array title: Location msg: type: string title: Message type: type: string title: Error Type type: object required: - loc - msg - type title: ValidationError securitySchemes: HTTPBearer: type: http description: HTTPBearer scheme: bearer ````