Quickstart

TimeGPT is a production-ready generative pretrained transformer for time series. It can accurately predict domains such as retail, electricity, finance, and IoT with just a few lines of code. Get started below!

Implementation Guide

Create a TimeGPT account and generate your API key

• Visit dashboard.nixtla.io to activate your free trial and create an account.
• 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 using the button on the right.

Dashboard displaying TimeGPT API keys and controls.

Install Nixtla

Install the Nixtla library in your preferred Python environment:

Install Nixtla
pip install nixtla

Import the Nixtla TimeGPT client

Import the Nixtla client and instantiate it with your API key:

Nixtla Client Setup
from nixtla import NixtlaClient

nixtla_client = NixtlaClient(
    api_key='my_api_key_provided_by_nixtla'
)

Verify the status and validity of your API key:

Validate API Key
nixtla_client.validate_api_key()

API Validation Output

Important: For enhanced security practices, see our guide on Setting Up your API Key.

Start making forecasts!

1. Load the AirPassengers Dataset
We will use the classic AirPassengers dataset to demonstrate forecasts.

2. Preview the Dataset
Quickly examine structures like timestamps and values before forecasting.

3. Plot the Time Series
Visualize historical data to understand trends or seasonality.

Load AirPassengers Data
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

Plot the dataset:

Plot AirPassengers Data
nixtla_client.plot(df, time_col='timestamp', target_col='value')

Historical AirPassengers data from 1949 to 1960.

Data Requirements

Saving Figures from TimeGPT

The plot method automatically displays figures in notebook environments. To save a plot locally:

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

Short and Long-Term Forecasting Examples

Generate a longer-term forecast

Forecast the next 12 months using the SDK’s forecast method:

Forecast 12 Months
timegpt_fcst_df = nixtla_client.forecast(
    df=df,
    h=12,
    freq='MS',
    time_col='timestamp',
    target_col='value'
)
timegpt_fcst_df.head()

Display the forecast:

Plot 12 Month Forecast
nixtla_client.plot(df, timegpt_fcst_df, time_col='timestamp', target_col='value')

12-month forecast for AirPassengers data.

You may also generate forecasts for longer horizons with the timegpt-1-long-horizon model. For example, 36 months ahead:

Forecast 36 Months Long Horizon
timegpt_fcst_df = nixtla_client.forecast(
    df=df,
    h=36,
    freq='MS',
    time_col='timestamp',
    target_col='value',
    model='timegpt-1-long-horizon'
)
timegpt_fcst_df.head()

Plot 36 Month Forecast
nixtla_client.plot(df, timegpt_fcst_df, time_col='timestamp', target_col='value')

36-month forecast using the 'timegpt-1-long-horizon' model.

Generate a shorter-term forecast

Forecast the next 6 months with a single command:

Forecast 6 Months
timegpt_fcst_df = nixtla_client.forecast(
    df=df,
    h=6,
    freq='MS',
    time_col='timestamp',
    target_col='value'
)
nixtla_client.plot(df, timegpt_fcst_df, time_col='timestamp', target_col='value')

6-month forecast for AirPassengers data.

QUICK START

GETTING STARTED

CAPABILITIES

DEPLOYMENT

TUTORIALS

USE CASES

REFERENCE

About

Implementation Guide

Start making forecasts!

Short and Long-Term Forecasting Examples

QUICK START

GETTING STARTED

CAPABILITIES

DEPLOYMENT

TUTORIALS

USE CASES

REFERENCE

About

​Implementation Guide

​Start making forecasts!

​Short and Long-Term Forecasting Examples

Implementation Guide

Start making forecasts!

Short and Long-Term Forecasting Examples