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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!

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Implementation Guide

1

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 for TimeGPT API keys

Dashboard displaying TimeGPT API keys and controls.

2

Install Nixtla

Install the Nixtla library in your preferred Python environment:
Install Nixtla
pip install nixtla
3

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()
Validation Log Output
INFO:nixtla.nixtla_client:Happy Forecasting! :), If you have questions or need support, please email support@nixtla.io

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

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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()
timestampvalue
01949-01-01112
11949-02-01118
21949-03-01132
31949-04-01129
41949-05-01121
Plot the dataset:
Plot AirPassengers Data
nixtla_client.plot(df, time_col='timestamp', target_col='value')
Time Series Plot

Historical AirPassengers data from 1949 to 1960.

  • The target variable must not contain missing or non-numeric values.
  • Date stamps must form a continuous sequence without gaps for the selected frequency.
  • Pandas should correctly parse the timestamp column (see Pandas documentation).
  • The forecast method does not fill or handle missing dates.
For more details, visit Data Requirements.
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')

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Short and Long-Term Forecasting Examples

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')
Forecasted Plot

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')
Longer Forecast Plot

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

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')
Shorter Forecast Plot

6-month forecast for AirPassengers data.

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