> ## 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 Online Multi Series Anomaly Detector > This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. 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, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. 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/online_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/online_anomaly_detection: post: summary: Foundational Time Series Model Online Multi Series Anomaly Detector description: >- This endpoint performs online anomaly detection based on the provided data. It uses cross-validation for more robust detection of anomalies and it supports detection for univariate and multivariate scenarios. 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, it provides the prediction interval used to define if an observation is an anomaly, and it reports the associated z-score for each point. Get your token for private beta at https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference. operationId: v2_online_anomaly_detection_v2_online_anomaly_detection_post requestBody: content: application/json: schema: $ref: '#/components/schemas/OnlineAnomalyInput' examples: - series: sizes: - 320 'y': - 12 - 12.99833416646828 - 13.986693307950611 - 14.955202066613396 - 15.894183423086506 - 16.794255386042032 - 17.646424733950354 - 18.442176872376912 - 19.173560908995228 - 19.833269096274833 - 20.414709848078964 - 20.912073600614356 - 21.320390859672266 - 21.63558185417193 - 21.854497299884603 - 21.974949866040546 - 21.995736030415053 - 21.916648104524686 - 21.73847630878195 - 21.463000876874144 - 21.092974268256818 - 20.632093666488736 - 20.0849640381959 - 19.457052121767198 - 18.754631805511508 - 17.984721441039564 - 17.15501371821464 - 16.2737988023383 - 15.349881501559047 - 14.39249329213982 - 13.411200080598672 - 12.415806624332905 - 11.416258565724199 - 10.422543058567513 - 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HTTPBearer: [] components: schemas: OnlineAnomalyInput: 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. detection_size: type: integer exclusiveMinimum: 0 title: Detection Size description: >- 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 less than 6 * detection_size forecasted samples. threshold_method: type: string enum: - univariate - multivariate title: Threshold Method description: The thresholding method to detect anomalies default: univariate h: type: integer exclusiveMinimum: 0 title: H description: >- The forecasting horizon. This represents the number of time steps into the future that the forecast should predict. 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 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 finetune_steps: type: integer minimum: 0 title: Finetune Steps description: >- 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. default: 0 finetune_loss: type: string enum: - default - mae - mse - rmse - mape - smape - poisson title: Finetune Loss description: >- The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers. default: default finetune_depth: type: integer enum: - 1 - 2 - 3 - 4 - 5 title: Finetune Depth description: >- 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. By default, the value is set to 1. default: 1 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 step_size: anyOf: - type: integer exclusiveMinimum: 0 - type: 'null' title: Step Size description: >- Step size between each cross validation window. If None it will be equal to the forecasting horizon. type: object required: - series - freq - detection_size - h title: OnlineAnomalyInput OnlineAnomalyOutput: 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 idxs: items: type: integer type: array title: Idxs anomaly: items: type: boolean type: array title: Anomaly anomaly_score: items: type: number type: array title: Anomaly Score accumulated_anomaly_score: anyOf: - items: type: number type: array - type: 'null' title: Accumulated Anomaly Score intervals: anyOf: - additionalProperties: items: type: number type: array type: object - type: 'null' title: Intervals type: object required: - input_tokens - output_tokens - finetune_tokens - mean - sizes - idxs - anomaly - anomaly_score title: OnlineAnomalyOutput 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 ````