Fine-tuning is a powerful process for utilizing TimeGPT more effectively. Foundation models such as TimeGPT are pre-trained on vast amounts of data, capturing wide-ranging features and patterns. These models can then be specialized for specific contexts or domains. With fine-tuning, the model’s parameters are refined to forecast a new task, allowing it to tailor its vast pre-existing knowledge towards the requirements of the new data. Fine-tuning thus serves as a crucial bridge, linking TimeGPT’s broad capabilities to your tasks specificities.

Concretely, the process of fine-tuning consists of performing a certain number of training iterations on your input data minimizing the forecasting error. The forecasts will then be produced with the updated model. To control the number of iterations, use the finetune_steps argument of the forecast method.

1. Import packages

First, we import the required packages and initialize the Nixtla client

import pandas as pd
from nixtla import NixtlaClient
nixtla_client = NixtlaClient(
    # defaults to os.environ.get("NIXTLA_API_KEY")
    api_key = 'my_api_key_provided_by_nixtla'


Use an Azure AI endpoint

To use an Azure AI endpoint, remember to set also the base_url argument:

nixtla_client = NixtlaClient(base_url="you azure ai endpoint", api_key="your api_key")

2. Load data

df = pd.read_csv('')

3. Fine-tuning

timegpt_fcst_finetune_df = nixtla_client.forecast(
    df=df, h=12, finetune_steps=10,
    time_col='timestamp', target_col='value',
INFO:nixtlats.nixtla_client:Validating inputs...
INFO:nixtlats.nixtla_client:Preprocessing dataframes...
INFO:nixtlats.nixtla_client:Inferred freq: MS
INFO:nixtlats.nixtla_client:Calling Forecast Endpoint...


Available models in Azure AI

If you are using an Azure AI endpoint, please be sure to set model="azureai":

nixtla_client.forecast(..., model="azureai")

For the public API, we support two models: timegpt-1 and timegpt-1-long-horizon.

By default, timegpt-1 is used. Please see this tutorial on how and when to use timegpt-1-long-horizon.

    df, timegpt_fcst_finetune_df, 
    time_col='timestamp', target_col='value',

In this code, finetune_steps=10 means the model will go through 10 iterations of training on your time series data.

Keep in mind that fine-tuning can be a bit of trial and error. You might need to adjust the number of finetune_steps based on your specific needs and the complexity of your data. It’s recommended to monitor the model’s performance during fine-tuning and adjust as needed. Be aware that more finetune_steps may lead to longer training times and could potentially lead to overfitting if not managed properly.

Remember, fine-tuning is a powerful feature, but it should be used thoughtfully and carefully.

For a detailed guide on using a specific loss function for fine-tuning, check out the Fine-tuning with a specific loss function tutorial.