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Quick Start

Get started with TimeGPT in minutes

SDK Installation

Set up the Python SDK for TimeGPT

Pricing

Review subscription plans and pricing

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Commonly asked questions

  • TimeGPT
  • API Key
  • Privacy & Security
  • Troubleshooting
  • Support
TimeGPT is the first foundation model for time series forecasting. It produces accurate forecasts for new time series across diverse domains using only historical values as inputs. The model reads time series data sequentially from left to right, similar to how humans read a sentence. It examines windows of past data as “tokens” and predicts what comes next based on identified patterns that extrapolate into the future. Beyond forecasting, TimeGPT supports other time series tasks, including what-if scenarios and anomaly detection.
TimeGPT is specifically designed for time series data, not text.
No, TimeGPT is not based on any large language model. While it follows the principle of training a large transformer model on a vast dataset, its architecture specifically handles time series data and minimizes forecasting errors.
To get started with TimeGPT, register for an account at dashboard.nixtla.io. After confirming your signup via email, you can access your dashboard with account details.

Sign up

Create an account at dashboard.nixtla.io

Confirm email

Click the confirmation link in your email

Get API key

Find your API key in the dashboard under “API Keys”

Install SDK

Run pip install nixtla to install the Python SDK
For a deeper understanding of TimeGPT, refer to the research paper. While some aspects of the model architecture remain confidential, registration for TimeGPT is open to everyone.
You can use TimeGPT through the Python SDK or the REST API.
  • Python SDK
  • REST API
Python SDK Forecast Example
from nixtla import NixtlaClient

# Initialize client with your API key
client = NixtlaClient(api_key="your_api_key")

# Make a forecast
forecast = client.forecast(df, h=7)
Both methods require an API key, obtained upon registration and available in your dashboard under “API Keys”.

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Features & Capabilities

  • Input Data
  • Forecasting
  • Advanced Features
  • Data Requirements
  • Visualization
  • Fine-tuning
TimeGPT accepts pandas dataframes in long format with these necessary columns:
You can also pass a DataFrame with a DatetimeIndex without the ds column.
Example Input DataFrame
import pandas as pd

# Create sample data
data = {
    'ds': ['2023-01-01', '2023-01-02', '2023-01-03'],
    'y': [10, 12, 15]
}

df = pd.DataFrame(data)
df['ds'] = pd.to_datetime(df['ds'])

print(df)
TimeGPT also works with distributed dataframes like dask, spark, and ray.
Yes, TimeGPT can forecast multiple time series simultaneously.
For guidance on forecasting multiple time series at once, consult the Multiple Series tutorial.
Multiple Series Forecasting
# Example of forecasting multiple series
from nixtla import NixtlaClient

# Initialize client
client = NixtlaClient(api_key="your_api_key")

# Group identifier for multiple series
df['unique_id'] = df['store_id'] + '_' + df['item_id']

# Forecast multiple series at once
forecast = client.forecast(df, h=7, level=[80, 90])
Need more help? Contact our support team.
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