TimeGPT FAQ

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SDK Installation

Set up the Python SDK for TimeGPT

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Commonly asked questions

What is TimeGPT?

Is TimeGPT based on a Large Language Model (LLM)?

How do I get started with TimeGPT?

How accessible is TimeGPT and what are the usage costs?

How can I use TimeGPT?

You can use TimeGPT through the Python SDK or the 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)

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)

REST API Forecast Example

curl -X POST "https://api.nixtla.io/timegpt" \
  -H "accept: application/json" \
  -H "x-api-key: your_api_key" \
  -H "Content-Type: application/json" \
  -d '{"df": [{"ds": "2023-01-01", "y": 100}, ...], "h": 7}'

Both methods require an API key, obtained upon registration and available in your dashboard under “API Keys”.

What is TimeGPT?

Is TimeGPT based on a Large Language Model (LLM)?

How do I get started with TimeGPT?

How accessible is TimeGPT and what are the usage costs?

How can I use TimeGPT?

You can use TimeGPT through the Python SDK or the 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)

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)

REST API Forecast Example

curl -X POST "https://api.nixtla.io/timegpt" \
  -H "accept: application/json" \
  -H "x-api-key: your_api_key" \
  -H "Content-Type: application/json" \
  -d '{"df": [{"ds": "2023-01-01", "y": 100}, ...], "h": 7}'

Both methods require an API key, obtained upon registration and available in your dashboard under “API Keys”.

What is an API key?

Where can I get an API key?

How do I use my API key?

To integrate your API key into your development workflow, refer to the Setting Up Your API Key tutorial.

Python API Key Example

from nixtla import NixtlaClient

client = NixtlaClient(api_key="your_api_key")

How can I check the status of my API key?

Check your API key status with the validate_api_key method of the NixtlaClient class.

Validate API Key Example

from nixtla import NixtlaClient

nixtla_client = NixtlaClient(api_key='my_api_key_provided_by_nixtla')
nixtla_client.validate_api_key()

Log Output

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

True

What if my API key isn't validating?

How do you ensure the privacy and security of my data?

Common errors and warnings

Error message: Invalid API key

Invalid API Key Error

ApiError: status_code: 401, body: {'data': None, 'message': 'Invalid API key', 'details': 'Key not found', 'code': 'A12', 'requestID': 'E7F2BBTB2P', 'support': 'If you have questions or need support, please email support@nixtla.io'}

This error occurs when your TimeGPT API key is invalid or not set up correctly. Use the validate_api_key method to verify it or check that you copied it correctly from the “API Keys” section of your dashboard.

Error message: Too many requests

Too Many Requests Error

ApiError: status_code: 429, body: {'data': None, 'message': 'Too many requests', 'details': 'You need to add a payment method to continue using the API, do so from https://dashboard.nixtla.io', 'code': 'A21', 'requestID': 'NCJDK7KSJ6', 'support': 'If you have questions or need support, please email support@nixtla.io'}

This error occurs when you have exhausted your free credits and need to add a payment method to continue using TimeGPT. Add a payment method in the “Billing” section of your dashboard.

Error message: WriteTimeout

Get Help with TimeGPT For more questions or support, reach out through one of our channels:

GitHub Issues

For technical questions or bugs

Email Support

For general inquiries or support

Slack Community

Connect with our team and community

When reporting issues, include your API key status, SDK version, and sample code to help us assist you more quickly.

Features & Capabilities

What is the input to TimeGPT?

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.

Can TimeGPT handle multiple time series?

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])

What is the input to TimeGPT?

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.

Can TimeGPT handle multiple time series?

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])

Does TimeGPT support forecasting with exogenous variables?

Yes, TimeGPT can incorporate external variables into forecasts.

For instructions on incorporating exogenous variables to TimeGPT, see the Exogenous Variables tutorial. For incorporating calendar dates, the Holidays and Special Dates tutorial might help. For categorical variables, refer to the Categorical Variables tutorial.

Exogenous Variables Forecast

# Forecasting with exogenous variables
forecast = client.forecast(
    df,
    h=7,
    X_df=exog_df  # DataFrame with exogenous variables
)

Can TimeGPT be used to forecast historical data?

Yes. To forecast historical data using TimeGPT, see the Historical Forecast tutorial.

Historical Forecast

# Get in-sample predictions
historical_forecast = client.forecast(
    df,
    h=7,
    add_history=True  # Include historical predictions
)

Historical vs. Future Forecasting

What is the maximum forecast horizon allowed by TimeGPT?

Can TimeGPT be used for anomaly detection?

Yes, TimeGPT includes anomaly detection capabilities.

To learn how to use TimeGPT for anomaly detection, refer to the Anomaly Detection tutorial.

Anomaly Detection Example

# Detect anomalies in time series
anomalies = client.detect_anomalies(df)

Anomaly Detection Example

Does TimeGPT support cross-validation?

Yes. To learn how to use TimeGPT for cross-validation, refer to the Cross-Validation tutorial.

Cross-Validation Example

# Perform cross-validation
cv_results = client.cross_validation(
    df,
    h=7,
    k=3,  # Number of folds
    test_size=7  # Size of each test fold
)

Can TimeGPT be used for uncertainty quantification?

Yes. For more information, explore the Prediction Intervals and Quantile Forecasts tutorials.

Prediction Intervals Example

# Generate prediction intervals
forecast_with_intervals = client.forecast(
    df,
    h=7,
    level=[80, 90, 95]  # Confidence levels
)

Forecast with Prediction Intervals

Can TimeGPT handle large datasets?

Yes, TimeGPT works with distributed computing frameworks for large datasets.

For large datasets with hundreds of thousands or millions of time series, we recommend using a distributed backend. TimeGPT works with several distributed computing frameworks, including Spark, Ray, and Dask.

Using Dask Example

import dask.dataframe as dd

# Convert to Dask DataFrame
dask_df = dd.from_pandas(df, npartitions=4)

# Forecast using Dask backend
forecast = client.forecast(dask_df, h=7)

Can TimeGPT be used with limited/short data?

Can TimeGPT handle missing values?

How can I plot the TimeGPT forecast?

The NixtlaClient class has a plot method for visualizing forecasts. This method works only in interactive environments such as Jupyter notebooks, not in Python scripts.

Plotting Forecast Example

# Plot forecast
client.plot(
    historical_data=df,
    forecast_data=forecast,
    level=[80, 95]  # Optional: show prediction intervals
)

Sample TimeGPT Forecast Visualization

Does TimeGPT support polars?

Does TimeGPT produce stable predictions?

Can TimeGPT forecast data with simple pattern such as a straight line or sine wave?

What is fine-tuning?

Fine-tuning improves TimeGPT’s performance for your specific data patterns.

TimeGPT was trained on the largest publicly available time series dataset, covering domains including finance, retail, healthcare, and more. This comprehensive training enables TimeGPT to produce accurate forecasts for new time series without additional training (zero-shot learning).

While the zero-shot model provides a solid baseline, TimeGPT performance often improves through fine-tuning. During this process, the TimeGPT model undergoes additional training using your specific dataset, starting from the pre-trained parameters.

Fine-tuning Example

# Fine-tune with 100 steps
forecast = client.forecast(
    df,
    h=7,
    finetune_steps=100,
    finetune_loss="mse"  # Mean Squared Error
)

For a comprehensive guide on fine-tuning, refer to the fine-tuning and fine-tuning with a specific loss function tutorials.

Do I have to fine-tune every series?

Can I save fine-tuned parameters?

Yes, you can save and reuse fine-tuned models.

You can fine-tune the TimeGPT model, save it, and reuse it later. For detailed instructions, see our guide on Re-using Fine-tuned Models.

Save Fine-tuned Model

# Fine-tune and save the model
fine_tuned_parameters = client.forecast(
    df,
    h=7,
    finetune_steps=100,
    return_model=True  # Return the fine-tuned parameters
)

# Save to file
import pickle
with open("fine_tuned_model.pkl", "wb") as f:
    pickle.dump(fine_tuned_parameters, f)

Save Fine-tuned Model

# Fine-tune and save the model
fine_tuned_parameters = client.forecast(
    df,
    h=7,
    finetune_steps=100,
    return_model=True  # Return the fine-tuned parameters
)

# Save to file
import pickle
with open("fine_tuned_model.pkl", "wb") as f:
    pickle.dump(fine_tuned_parameters, f)

Load Fine-tuned Model

# Load the fine-tuned parameters
import pickle
with open("fine_tuned_model.pkl", "rb") as f:
    fine_tuned_parameters = pickle.load(f)

# Use the fine-tuned model
forecast = client.forecast(
    new_df,
    h=7,
    model=fine_tuned_parameters
)

Need more help? Contact our support team.

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Features & Capabilities

QUICK START

GETTING STARTED

CAPABILITIES

DEPLOYMENT

TUTORIALS

USE CASES

REFERENCE

About

Quick Start

SDK Installation

Pricing

​Commonly asked questions

Common errors and warnings

GitHub Issues

Email Support

Slack Community

​Features & Capabilities

Commonly asked questions

Features & Capabilities