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.

Key Benefit

Azure-native generative forecasting with TimeGEN-1 for streamlined deployments.

Use Cases

• Demand forecasting\• Electricity load prediction\• Financial time series\• IoT data analysis
1

Step 1: Set up a TimeGEN-1 endpoint on Azure and generate an API key

  1. Visit 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.
TimeGEN-1 model catalog deployment option

TimeGEN-1 model catalog deployment option.

  1. Click Endpoint in the sidebar.
  2. Copy the base URL and API Key shown for your TimeGEN-1 endpoint.
Endpoint URL and API key

Endpoint URL and API key for TimeGEN-1.

2

Step 2: Install Nixtla Python SDK

Install the nixtla package using pip:
Install nixtla SDK
pip install nixtla
3

Step 3: Import and instantiate the Nixtla client

Import the Nixtla client into your Python environment:
Import NixtlaClient
from nixtla import NixtlaClient
Then create a client instance using your TimeGEN-1 endpoint credentials:
Instantiate NixtlaClient
nixtla_client = NixtlaClient(
    base_url="YOUR_BASE_URL",
    api_key="YOUR_API_KEY"
)
4

Step 4: Load your time series data

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.
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:
Visualize time series
nixtla_client.plot(df, time_col='timestamp', target_col='value')
AirPassengers time series visualization

AirPassengers time series sample visualized.

5

Step 5: Generate forecasts

Use the forecast method from the Nixtla client to forecast the next 12 months.

Parameters

• df: Pandas DataFrame with time series data\• h: Forecast horizon (number of steps ahead)\• freq: Time series frequency (pandas frequency aliases)\• time_col: Name of timestamp column\• target_col: Name of forecast variable
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.
Example output:
timestampTimeGPT
01961-01-01437.837921
11961-02-01426.062714
21961-03-01463.116547
31961-04-01478.244507
41961-05-01505.646484
Visualize the forecast results:
Visualize forecast results
nixtla_client.plot(df, timegen_fcst_df, time_col='timestamp', target_col='value')
Forecast visualization AirPassengers

Forecast visualization for the AirPassengers dataset.