---
title: "Foundational Time Series Model Multi Series Cross Validation (Beta)"
method: POST
path: "/cross_validation_multi_series"
tags: ["hidden"]
---

# Foundational Time Series Model Multi Series Cross Validation (Beta)

`POST /cross_validation_multi_series`

Perform Cross Validation for multiple series

## Request body

- MultiSeriesCrossValidation
  - `fewshot_steps` integer, nullable — Deprecated. Please use finetune_steps instead.
  - `fewshot_loss` 'default' | 'mae' | 'mse' | 'rmse' | 'mape' | 'smape', nullable — Deprecated. Please use finetune_loss instead.
  - `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.
  - `freq` string — 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.
  - `level` union[], nullable — 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.
    - union
      - integer
      - number
  - `fh` integer — The forecasting horizon. This represents the number of time steps into the future that the forecast should predict.
  - `y` unknown
  - `x` MultiSeriesInput
    - `columns` string[], required
    - `data` unknown[], required
      - unknown
  - `n_windows` integer — Number of windows to evaluate.
  - `step_size` integer, nullable — Step size between each cross validation window. If None it will be equal to the forecasting horizon.
  - `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.
  - `finetune_steps` integer — 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.
  - `finetune_loss` 'default' | 'mae' | 'mse' | 'rmse' | 'mape' | 'smape' — 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.

## Response `200`

Successful Response

- unknown

## Other responses

- `422` — Validation Error

---

[API](https://skmtc.dev/nixtla/apis/nixtla-forecast-api.md) · [All operations](https://skmtc.dev/nixtla/apis/nixtla-forecast-api/llms.txt) · [OpenAPI document](https://skmtc-service-production.skmtc.workers.dev/v1/apis/nixtla/nixtla-forecast-api/revisions/418dbfd4c8ef/schema)