---
title: "TimeGEN-1 Quickstart (Azure)"
description: "Quickstart guide to deploy and use TimeGEN-1 on Azure with the Nixtla Python SDK for time series forecasting."
icon: "rocket"
---
TimeGEN-1 is TimeGPT optimized for Azure infrastructure. It is a production-ready generative pretrained transformer for time series, capable of accurately predicting domains such as retail, electricity, finance, and IoT with minimal code.
Azure-native generative forecasting with TimeGEN-1 for streamlined deployments.
• Demand forecasting\
• Electricity load prediction\
• Financial time series\
• IoT data analysis
1. Visit [ml.azure.com](https://ml.azure.com) and sign in (or create a Microsoft account if needed).
2. Click **Models** in the sidebar.
3. Search for **TimeGEN** in the catalog and select **TimeGEN-1**.
4. Click **Deploy** to create an endpoint.
5. Click **Endpoint** in the sidebar.
6. Copy the **base URL** and **API Key** shown for your TimeGEN-1 endpoint.
Install the **nixtla** package using pip:
```shell Install nixtla SDK
pip install nixtla
```
Import the Nixtla client into your Python environment:
```python Import NixtlaClient
from nixtla import NixtlaClient
```
Then create a client instance using your TimeGEN-1 endpoint credentials:
```python Instantiate NixtlaClient
nixtla_client = NixtlaClient(
base_url="YOUR_BASE_URL",
api_key="YOUR_API_KEY"
)
```
In this example, we'll use the classic **AirPassengers** dataset to demonstrate forecasting. The dataset shows monthly passenger counts in Australia between 1949 and 1960.
```python Load AirPassengers dataset
import pandas as pd
df = pd.read_csv(
'https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/air_passengers.csv'
)
df.head()
```
Use the Nixtla client to quickly visualize your data:
```python Visualize time series
nixtla_client.plot(df, time_col='timestamp', target_col='value')
```
• Ensure the target column has no missing or non-numeric values.\
• Avoid gaps in date stamps (for the specific frequency) from the initial to final timestamp—missing dates are not automatically imputed.\
• Datestamps must be in a pandas-readable format. ([See Pandas reference](https://pandas.pydata.org/docs/reference/api/pandas.to_datetime.html))
See [Data Requirements](/data_requirements/data_requirements) for details.
In most notebook environments, figures display automatically. To save a figure locally, run:
```python Save plot figure
fig = nixtla_client.plot(df, time_col='timestamp', target_col='value')
fig.savefig('plot.png', bbox_inches='tight')
```
Use the `forecast` method from the Nixtla client to forecast the next 12 months.
• `df`: Pandas DataFrame with time series data\
• `h`: Forecast horizon (number of steps ahead)\
• `freq`: Time series frequency ([pandas frequency aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases))\
• `time_col`: Name of timestamp column\
• `target_col`: Name of forecast variable
```python Generate 12-month forecast
timegen_fcst_df = nixtla_client.forecast(
df=df,
h=12,
freq='MS',
time_col='timestamp',
target_col='value'
)
timegen_fcst_df.head()
```
Forecast endpoint call logs will be displayed for validation and preprocessing steps.
```bash Forecast API call logs
INFO:nixtla.nixtla_client:Validating inputs...
INFO:nixtla.nixtla_client:Preprocessing dataframes...
INFO:nixtla.nixtla_client:Inferred freq: MS
INFO:nixtla.nixtla_client:Restricting input...
INFO:nixtla.nixtla_client:Calling Forecast Endpoint...
```
Example output:
| | timestamp | TimeGPT |
| --- | ---------- | ---------- |
| 0 | 1961-01-01 | 437.837921 |
| 1 | 1961-02-01 | 426.062714 |
| 2 | 1961-03-01 | 463.116547 |
| 3 | 1961-04-01 | 478.244507 |
| 4 | 1961-05-01 | 505.646484 |
Visualize the forecast results:
```python Visualize forecast results
nixtla_client.plot(df, timegen_fcst_df, time_col='timestamp', target_col='value')
```
