Foundational Time Series Model Multi Series Finetuning

curl --request POST \
  --url https://api.nixtla.io/v2/finetune \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "series": {
    "y": [
      123
    ],
    "sizes": [
      123
    ],
    "X_future": [
      [
        123
      ]
    ],
    "X": [
      [
        123
      ]
    ],
    "categorical_exog": [
      1
    ]
  },
  "freq": "<string>",
  "model": "timegpt-1",
  "finetune_steps": 10,
  "finetune_loss": "default",
  "finetune_depth": 1,
  "output_model_id": "<string>",
  "finetuned_model_id": "<string>",
  "hist_exog": [
    1
  ],
  "multivariate": false,
  "model_parameters": {}
}
'

import requests

url = "https://api.nixtla.io/v2/finetune"

payload = {
    "series": {
        "y": [123],
        "sizes": [123],
        "X_future": [[123]],
        "X": [[123]],
        "categorical_exog": [1]
    },
    "freq": "<string>",
    "model": "timegpt-1",
    "finetune_steps": 10,
    "finetune_loss": "default",
    "finetune_depth": 1,
    "output_model_id": "<string>",
    "finetuned_model_id": "<string>",
    "hist_exog": [1],
    "multivariate": False,
    "model_parameters": {}
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    series: {y: [123], sizes: [123], X_future: [[123]], X: [[123]], categorical_exog: [1]},
    freq: '<string>',
    model: 'timegpt-1',
    finetune_steps: 10,
    finetune_loss: 'default',
    finetune_depth: 1,
    output_model_id: '<string>',
    finetuned_model_id: '<string>',
    hist_exog: [1],
    multivariate: false,
    model_parameters: {}
  })
};

fetch('https://api.nixtla.io/v2/finetune', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.nixtla.io/v2/finetune",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'series' => [
        'y' => [
                123
        ],
        'sizes' => [
                123
        ],
        'X_future' => [
                [
                                123
                ]
        ],
        'X' => [
                [
                                123
                ]
        ],
        'categorical_exog' => [
                1
        ]
    ],
    'freq' => '<string>',
    'model' => 'timegpt-1',
    'finetune_steps' => 10,
    'finetune_loss' => 'default',
    'finetune_depth' => 1,
    'output_model_id' => '<string>',
    'finetuned_model_id' => '<string>',
    'hist_exog' => [
        1
    ],
    'multivariate' => false,
    'model_parameters' => [
        
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.nixtla.io/v2/finetune"

	payload := strings.NewReader("{\n  \"series\": {\n    \"y\": [\n      123\n    ],\n    \"sizes\": [\n      123\n    ],\n    \"X_future\": [\n      [\n        123\n      ]\n    ],\n    \"X\": [\n      [\n        123\n      ]\n    ],\n    \"categorical_exog\": [\n      1\n    ]\n  },\n  \"freq\": \"<string>\",\n  \"model\": \"timegpt-1\",\n  \"finetune_steps\": 10,\n  \"finetune_loss\": \"default\",\n  \"finetune_depth\": 1,\n  \"output_model_id\": \"<string>\",\n  \"finetuned_model_id\": \"<string>\",\n  \"hist_exog\": [\n    1\n  ],\n  \"multivariate\": false,\n  \"model_parameters\": {}\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.nixtla.io/v2/finetune")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"series\": {\n    \"y\": [\n      123\n    ],\n    \"sizes\": [\n      123\n    ],\n    \"X_future\": [\n      [\n        123\n      ]\n    ],\n    \"X\": [\n      [\n        123\n      ]\n    ],\n    \"categorical_exog\": [\n      1\n    ]\n  },\n  \"freq\": \"<string>\",\n  \"model\": \"timegpt-1\",\n  \"finetune_steps\": 10,\n  \"finetune_loss\": \"default\",\n  \"finetune_depth\": 1,\n  \"output_model_id\": \"<string>\",\n  \"finetuned_model_id\": \"<string>\",\n  \"hist_exog\": [\n    1\n  ],\n  \"multivariate\": false,\n  \"model_parameters\": {}\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.nixtla.io/v2/finetune")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"series\": {\n    \"y\": [\n      123\n    ],\n    \"sizes\": [\n      123\n    ],\n    \"X_future\": [\n      [\n        123\n      ]\n    ],\n    \"X\": [\n      [\n        123\n      ]\n    ],\n    \"categorical_exog\": [\n      1\n    ]\n  },\n  \"freq\": \"<string>\",\n  \"model\": \"timegpt-1\",\n  \"finetune_steps\": 10,\n  \"finetune_loss\": \"default\",\n  \"finetune_depth\": 1,\n  \"output_model_id\": \"<string>\",\n  \"finetuned_model_id\": \"<string>\",\n  \"hist_exog\": [\n    1\n  ],\n  \"multivariate\": false,\n  \"model_parameters\": {}\n}"

response = http.request(request)
puts response.read_body

{
  "input_tokens": 1,
  "output_tokens": 1,
  "finetune_tokens": 1,
  "finetuned_model_id": "<string>"
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>",
      "input": "<unknown>",
      "ctx": {}
    }
  ]
}

API Reference

Foundational Time Series Model Multi Series Finetuning

Fine-tune the large time model to your data and save it for later use. 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 ID of the finetuned model, which you can provide in other endpoints to use that model to make the forecasts. Get your token for private beta at https://www.nixtla.io/book-a-free-trial?utm_source=nixtla.io&utm_campaign=/docs/api-reference.

POST

finetune

curl --request POST \
  --url https://api.nixtla.io/v2/finetune \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "series": {
    "y": [
      123
    ],
    "sizes": [
      123
    ],
    "X_future": [
      [
        123
      ]
    ],
    "X": [
      [
        123
      ]
    ],
    "categorical_exog": [
      1
    ]
  },
  "freq": "<string>",
  "model": "timegpt-1",
  "finetune_steps": 10,
  "finetune_loss": "default",
  "finetune_depth": 1,
  "output_model_id": "<string>",
  "finetuned_model_id": "<string>",
  "hist_exog": [
    1
  ],
  "multivariate": false,
  "model_parameters": {}
}
'

import requests

url = "https://api.nixtla.io/v2/finetune"

payload = {
    "series": {
        "y": [123],
        "sizes": [123],
        "X_future": [[123]],
        "X": [[123]],
        "categorical_exog": [1]
    },
    "freq": "<string>",
    "model": "timegpt-1",
    "finetune_steps": 10,
    "finetune_loss": "default",
    "finetune_depth": 1,
    "output_model_id": "<string>",
    "finetuned_model_id": "<string>",
    "hist_exog": [1],
    "multivariate": False,
    "model_parameters": {}
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    series: {y: [123], sizes: [123], X_future: [[123]], X: [[123]], categorical_exog: [1]},
    freq: '<string>',
    model: 'timegpt-1',
    finetune_steps: 10,
    finetune_loss: 'default',
    finetune_depth: 1,
    output_model_id: '<string>',
    finetuned_model_id: '<string>',
    hist_exog: [1],
    multivariate: false,
    model_parameters: {}
  })
};

fetch('https://api.nixtla.io/v2/finetune', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.nixtla.io/v2/finetune",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'series' => [
        'y' => [
                123
        ],
        'sizes' => [
                123
        ],
        'X_future' => [
                [
                                123
                ]
        ],
        'X' => [
                [
                                123
                ]
        ],
        'categorical_exog' => [
                1
        ]
    ],
    'freq' => '<string>',
    'model' => 'timegpt-1',
    'finetune_steps' => 10,
    'finetune_loss' => 'default',
    'finetune_depth' => 1,
    'output_model_id' => '<string>',
    'finetuned_model_id' => '<string>',
    'hist_exog' => [
        1
    ],
    'multivariate' => false,
    'model_parameters' => [
        
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://api.nixtla.io/v2/finetune"

	payload := strings.NewReader("{\n  \"series\": {\n    \"y\": [\n      123\n    ],\n    \"sizes\": [\n      123\n    ],\n    \"X_future\": [\n      [\n        123\n      ]\n    ],\n    \"X\": [\n      [\n        123\n      ]\n    ],\n    \"categorical_exog\": [\n      1\n    ]\n  },\n  \"freq\": \"<string>\",\n  \"model\": \"timegpt-1\",\n  \"finetune_steps\": 10,\n  \"finetune_loss\": \"default\",\n  \"finetune_depth\": 1,\n  \"output_model_id\": \"<string>\",\n  \"finetuned_model_id\": \"<string>\",\n  \"hist_exog\": [\n    1\n  ],\n  \"multivariate\": false,\n  \"model_parameters\": {}\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.nixtla.io/v2/finetune")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"series\": {\n    \"y\": [\n      123\n    ],\n    \"sizes\": [\n      123\n    ],\n    \"X_future\": [\n      [\n        123\n      ]\n    ],\n    \"X\": [\n      [\n        123\n      ]\n    ],\n    \"categorical_exog\": [\n      1\n    ]\n  },\n  \"freq\": \"<string>\",\n  \"model\": \"timegpt-1\",\n  \"finetune_steps\": 10,\n  \"finetune_loss\": \"default\",\n  \"finetune_depth\": 1,\n  \"output_model_id\": \"<string>\",\n  \"finetuned_model_id\": \"<string>\",\n  \"hist_exog\": [\n    1\n  ],\n  \"multivariate\": false,\n  \"model_parameters\": {}\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.nixtla.io/v2/finetune")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"series\": {\n    \"y\": [\n      123\n    ],\n    \"sizes\": [\n      123\n    ],\n    \"X_future\": [\n      [\n        123\n      ]\n    ],\n    \"X\": [\n      [\n        123\n      ]\n    ],\n    \"categorical_exog\": [\n      1\n    ]\n  },\n  \"freq\": \"<string>\",\n  \"model\": \"timegpt-1\",\n  \"finetune_steps\": 10,\n  \"finetune_loss\": \"default\",\n  \"finetune_depth\": 1,\n  \"output_model_id\": \"<string>\",\n  \"finetuned_model_id\": \"<string>\",\n  \"hist_exog\": [\n    1\n  ],\n  \"multivariate\": false,\n  \"model_parameters\": {}\n}"

response = http.request(request)
puts response.read_body

{
  "input_tokens": 1,
  "output_tokens": 1,
  "finetune_tokens": 1,
  "finetuned_model_id": "<string>"
}

{
  "detail": [
    {
      "loc": [
        "<string>"
      ],
      "msg": "<string>",
      "type": "<string>",
      "input": "<unknown>",
      "ctx": {}
    }
  ]
}

Authorizations

Authorization

string

header

required

HTTPBearer

Body

application/json

series

SeriesWithFutureExogenous · object

required

Show child attributes

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

default:timegpt-1

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.

finetune_steps

integer

default:10

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.

Required range: x >= 0

finetune_loss

enum<string>

default:default

The loss used to train the large time model on the data. Select from ['default', 'mae', 'mse', 'rmse', 'mape', 'smape', 'poisson']. It will only be used if finetune_steps larger than 0. Default is a robust loss function that is less sensitive to outliers.

Available options:

default,

mae,

mse,

rmse,

mape,

smape,

poisson

finetune_depth

enum<integer>

default:1

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. Note that this parameter is only effective for timegpt-1 and timegpt-1-long-horizon models, meanwhile it has no effect on the other models. By default, the value is set to 1.

Available options:

1,

2,

3,

4,

5

output_model_id

string | null

ID to assign to the finetuned model

Pattern: ^[a-zA-Z0-9\-_]{1,36}$

finetuned_model_id

string | null

ID of previously finetuned model

Pattern: ^[a-zA-Z0-9\-_]{1,36}$

hist_exog

integer[] | null

Zero-based indices of the exogenous features to treat as historical.

Required range: x >= 0

multivariate

boolean

default:false

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

Model Parameters · object | null

Optional dictionary of parameters to customize the behavior of the large time model.

Response

Successful Response

input_tokens

integer

required

Required range: x >= 0

output_tokens

integer

required

Required range: x >= 0

finetune_tokens

integer

required

Required range: x >= 0

finetuned_model_id

string

required

Pattern: ^[a-zA-Z0-9\-_]{1,36}$

Foundational Time Series Model Multi Series Cross Validation List Fine-tuned Models

⌘I

INTRODUCTION

SETUP

DATA REQUIREMENTS

FORECASTING

ANOMALY DETECTION

USE CASES

REFERENCE

About

Foundational Time Series Model Multi Series Finetuning

Authorizations

Body

Response