> ## 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 Historic (Deprecated)

> **Deprecated:** This endpoint is deprecated and will be removed in a future release. Please use [`/v2/cross_validation`](#tag/default/POST/v2/cross_validation) instead, which offers equivalent in-sample evaluation capabilities through rolling-window cross validation.

Based on the provided data, this endpoint predicts the in-sample period (historical period) values 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 the predicted values for the historical period. Usually useful for anomaly detection. 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/historic_forecast
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/historic_forecast:
    post:
      summary: Foundational Time Series Model Multi Series Historic (Deprecated)
      description: >-
        **Deprecated:** This endpoint is deprecated and will be removed in a
        future release. Please use
        [`/v2/cross_validation`](#tag/default/POST/v2/cross_validation) instead,
        which offers equivalent in-sample evaluation capabilities through
        rolling-window cross validation.


        Based on the provided data, this endpoint predicts the in-sample period
        (historical period) values 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 the predicted values for the historical period.
        Usually useful for anomaly detection. Get your token for private beta at
        https://nixtla.io/free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.
      operationId: v2_historic_forecast_v2_historic_forecast_post
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/InSampleInput'
              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
        required: true
      responses:
        '200':
          description: Successful Response
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/InSampleOutput'
        '422':
          description: Validation Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/HTTPValidationError'
      deprecated: true
      security:
        - HTTPBearer: []
components:
  schemas:
    InSampleInput:
      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:
            - items:
                anyOf:
                  - type: integer
                    exclusiveMaximum: 100
                    minimum: 0
                  - type: number
                    exclusiveMaximum: 100
                    minimum: 0
              type: array
              minItems: 1
            - type: 'null'
          title: Level
          description: >-
            A list of values representing the prediction intervals. Each value
            is a percentage that indicates the level of certainty for the
            corresponding prediction interval. For example, [80, 90] defines 80%
            and 90% prediction intervals.
        feature_contributions:
          type: boolean
          title: Feature Contributions
          description: Compute the exogenous features contributions to the forecast.
          default: false
      type: object
      required:
        - series
        - freq
      title: InSampleInput
    InSampleOutput:
      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
      type: object
      required:
        - input_tokens
        - output_tokens
        - finetune_tokens
        - mean
        - sizes
      title: InSampleOutput
    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

````