TimeGEN-1 Quickstart (Azure)

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

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

Visit ml.azure.com and sign in (or create a Microsoft account if needed).
Click Models in the sidebar.
Search for TimeGEN in the catalog and select TimeGEN-1.
Click Deploy to create an endpoint.

TimeGEN-1 model catalog deployment option.

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

Step 2: Install Nixtla Python SDK

Install the nixtla package using pip:

Install nixtla SDK

pip install nixtla

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"
)

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.

Data Requirements

• 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)

See Data Requirements for details.

Saving Figures

In most notebook environments, figures display automatically. To save a figure locally, run:

Save plot figure

fig = nixtla_client.plot(df, time_col='timestamp', target_col='value')
fig.savefig('plot.png', bbox_inches='tight')

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.

Forecast API Call Log

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:

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.

INTRODUCTION

SETUP

DATA REQUIREMENTS

FORECASTING

ANOMALY DETECTION

USE CASES

REFERENCE

About

Key Benefit

Use Cases

Parameters