> ## 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.

# Quickstart (TimeGPT-2)

> Learn how to use TimeGPT-2 family of time series forecasting models

## TimeGPT-2 Family of Foundation Models

[TimeGPT-2](https://www.nixtla.io/blog/timegpt-2-announcement) and [TimeGPT-2.1](https://www.nixtla.io/blog/timegpt-2-1-announcement) are the latest versions of our enterprise-grade models, built to reliably solve mission-critical time-series problems. The TimeGPT-2 family of models is optimized for enterprise needs, prioritizing accuracy and stability with a privacy-first approach and full support for self-hosted and on-premises deployments.

## Set Up TimeGPT-2 family of models for Python Time Series Forecasting

### Step 1: Confirm Access and get an API Key

* Confirm with [support@nixtla.io](mailto:support@nixtla.io) that your account has access to these latest models.
* Get your API key from [dashboard](https://nixtla.io/free-trial?utm_source=nixtla.io\&utm_campaign=/docs/forecasting/timegpt_2_family). Note that you can also use your existing keys as long as your account has access to these latest models (Step 1 above).
  * Sign in using Google, GitHub, or your email.
  * Navigate to **API Keys** in the menu and select **Create New API Key**.
  * Your new API key will appear on the screen. Copy this key and save it in a safe place for use later.

<Frame caption="TimeGPT dashboard showing API key management interface for Python forecasting">
  ![Dashboard for TimeGPT API keys](https://github.com/Nixtla/nixtla/blob/main/nbs/img/dashboard.png?raw=true)
</Frame>

### Step 2: Install Nixtla

Install the Nixtla library in your preferred Python environment. In order to use the TimeGPT-2 family of models, the client version must be >= 0.7.0.

```bash theme={null}
pip install nixtla>=0.7.0
```

You can verify the client version installed using the following code. It should return a version >= 0.7.0

```python theme={null}
from nixtla import __version__
print(__version__)
```

```bash theme={null}
0.7.2
```

### Step 3: Import the Nixtla TimeGPT client

Import the Nixtla client and instantiate it with your API key and base URL for TimeGPT-2 family:

```python theme={null}
from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    base_url = 'https://api-preview.nixtla.io',  # Needed for TimeGPT-2 family
    api_key='my_api_key_provided_by_nixtla'
)
```

Verify the status and validity of your API key:

```python theme={null}
nixtla_client.validate_api_key()
```

```bash theme={null}
True
```

## Forecasting with TimeGPT-2 family

### Load your time series data

```python theme={null}
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv')
df.head()
```

|   | timestamp  | value |
| - | ---------- | ----- |
| 0 | 1949-01-01 | 112   |
| 1 | 1949-02-01 | 118   |
| 2 | 1949-03-01 | 132   |
| 3 | 1949-04-01 | 129   |
| 4 | 1949-05-01 | 121   |

### Generate the forecast

Forecast the next 12 months using the SDK's `forecast` method. You can switch the model to any of the TimeGPT-2 family of models - `timegpt-2-pro`, `timegpt-2-lab`, `timegpt-2-mini`, `timegpt-2.1`

```python theme={null}
timegpt_fcst_df = nixtla_client.forecast(
    df,
    h=12,
    time_col="timestamp",
    target_col="value",
    model="timegpt-2.1",
)
```

### Plot the forecast

```python theme={null}
nixtla_client.plot(df, timegpt_fcst_df, time_col='timestamp', target_col='value')
```

<Frame caption="TimeGPT 12-month forecast visualization with confidence intervals for AirPassengers dataset">
  ![Forecasted Plot](https://raw.githubusercontent.com/Nixtla/nixtla/readme_docs/nbs/_docs/docs/getting-started/2_quickstart_files/figure-markdown_strict/cell-15-output-1.png)
</Frame>

## Summary

Using the TimeGPT-2 family of models is similar to using the TimeGPT-1 family with the following changes. In order to use TimeGPT-2 family of models

* Make sure that your account has access to these models
* Install the latest Nixtla client (>= 0.7.0)
* Make sure you use the right `base_url` while instantiating the client along with your API key.

Happy forecasting!