TimeGPT FAQ
Frequently asked questions about TimeGPT
Quick Start
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SDK Installation
Set up the Python SDK for TimeGPT
Pricing
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Commonly asked questions
What is TimeGPT?
What is TimeGPT?
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.
Is TimeGPT based on a Large Language Model (LLM)?
Is TimeGPT based on a Large Language Model (LLM)?
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.
How do I get started with TimeGPT?
How do I get started with TimeGPT?
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
How accessible is TimeGPT and what are the usage costs?
How accessible is TimeGPT and what are the usage costs?
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.
How can I use TimeGPT?
How can I use TimeGPT?
You can use TimeGPT through the Python SDK or the REST API.
Both methods require an API key, obtained upon registration and available in your dashboard under “API Keys”.
What is TimeGPT?
What is TimeGPT?
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.
Is TimeGPT based on a Large Language Model (LLM)?
Is TimeGPT based on a Large Language Model (LLM)?
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.
How do I get started with TimeGPT?
How do I get started with TimeGPT?
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
How accessible is TimeGPT and what are the usage costs?
How accessible is TimeGPT and what are the usage costs?
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.
How can I use TimeGPT?
How can I use TimeGPT?
You can use TimeGPT through the Python SDK or the REST API.
Both methods require an API key, obtained upon registration and available in your dashboard under “API Keys”.
What is an API key?
What is an API key?
An API key is a unique string of characters that authenticates your requests when using the Nixtla SDK, ensuring only authorized users can make requests.
Your API key is personal and should not be shared with anyone or exposed in client-side code.
Where can I get an API key?
Where can I get an API key?
Upon registration, you receive an API key available in your dashboard under “API Keys”. Keep your API key confidential.
Example API Key location in dashboard
How do I use my 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.
How can I check the status of my 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.
What if my API key isn't validating?
What if my API key isn't validating?
When you validate your API key and it returns False
:
- If you are targeting an Azure endpoint, getting
False
from theNixtlaClient.validate_api_key
method is expected. You can skip this step when targeting an Azure endpoint and proceed diretly to forecasting instead. - If you are not taregting an Azure endpoint, then you should check the following:
- Make sure you are using the latest version of the SDK (Python or R).
- Check that your API key is active in your dashboard by visiting https://dashboard.nixtla.io/.
- Consider any firewalls your organization might have. There may be restricted access. If so, you can whitelist our endpoint https://api.nixtla.io/.
- To use Nixtla’s API, you need to let your system know that our endpoint is ok, so it will let you access it. Whitelisting the endpoint isn’t something that Nixtla can do on our side. It’s something that needs to be done on the user’s system. This is a bit of an overview on whitelisting.
- If you work in an organization, please work with an IT team. They’re likely the ones setting the security and you can talk with them to get it addressed. If you run your own systems, then it’s something you should be able to update, depending on the system you’re using.
How do you ensure the privacy and security of my data?
How do you ensure the privacy and security of my data?
At Nixtla, we take privacy and security very seriously. To ensure you understand our data policies, refer to these documents:
Privacy Notice
Our data privacy policies
License Agreement
Python SDK license
Terms and Conditions
TimeGPT service terms
We are also developing a self-hosted version of TimeGPT for enterprise data with unique security requirements. This version is currently in beta. If interested in this option, contact us at support@nixtla.io
.
Common errors and warnings
Error message: Invalid API key
Error message: Invalid API key
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
Error message: Too many requests
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
Error message: WriteTimeout
A WriteTimeout
error indicates the request exceeded allowable processing time. This commonly happens with large datasets. To fix this, increase the num_partitions
parameter in the forecast
method of the NixtlaClient
class, or use a distributed backend.
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?
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.
TimeGPT also works with distributed dataframes like dask
, spark
, and ray
.
Can TimeGPT handle multiple time series?
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.
What is the input to TimeGPT?
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.
TimeGPT also works with distributed dataframes like dask
, spark
, and ray
.
Can TimeGPT handle multiple time series?
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.
Does TimeGPT support forecasting with exogenous variables?
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.
Can TimeGPT be used to forecast historical data?
Can TimeGPT be used to forecast historical data?
Yes. To forecast historical data using TimeGPT, see the Historical Forecast tutorial.
Historical vs. Future Forecasting
What is the maximum forecast horizon allowed by TimeGPT?
What is the maximum forecast horizon allowed by TimeGPT?
TimeGPT has no maximum forecast horizon, but performance decreases as the horizon increases. When the forecast horizon exceeds the data’s seasonal length (for example, more than 12 months for monthly data), you will receive this message:
WARNING:nixtla.nixtla_client:The specified horizon "h" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon
For details, refer to the Long Horizon in Time Series tutorial.
For best results, keep your forecast horizon within the seasonal pattern of your data.
Can TimeGPT be used for anomaly detection?
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
Does TimeGPT support cross-validation?
Does TimeGPT support cross-validation?
Yes. To learn how to use TimeGPT for cross-validation, refer to the Cross-Validation tutorial.
Can TimeGPT be used for uncertainty quantification?
Can TimeGPT be used for uncertainty quantification?
Yes. For more information, explore the Prediction Intervals and Quantile Forecasts tutorials.
Forecast with Prediction Intervals
Can TimeGPT handle large datasets?
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.
Can TimeGPT be used with limited/short data?
Can TimeGPT be used with limited/short data?
TimeGPT supports any amount of data for generating point forecasts and can produce results with just one observation per series. When using arguments such as level
, finetune_steps
, X_df
(exogenous variables), or add_history
, additional data points are necessary depending on data frequency. For more details, refer to the Data Requirements tutorial.
While TimeGPT can work with minimal data, more historical data typically produces better forecasts.
Can TimeGPT handle missing values?
Can TimeGPT handle missing values?
TimeGPT cannot handle missing values or series with irregular timestamps.
For more information, see the Forecasting Time Series with Irregular Timestamps and Dealing with Missing Values tutorials.
How can I plot the TimeGPT forecast?
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.
Sample TimeGPT Forecast Visualization
Does TimeGPT support polars?
Does TimeGPT support polars?
Currently, TimeGPT does not support polars.
Does TimeGPT produce stable predictions?
Does TimeGPT produce stable predictions?
Yes, TimeGPT produces consistent results for identical inputs.
TimeGPT is engineered for stability, ensuring consistent results for identical input data. Given the same dataset, the model will produce the same forecasts.
Can TimeGPT forecast data with simple pattern such as a straight line or sine wave?
Can TimeGPT forecast data with simple pattern such as a straight line or sine wave?
While not the primary use case for TimeGPT, it can generate solid results on simple data patterns like straight lines. Zero-shot predictions might not always meet expectations, but fine-tuning allows TimeGPT to quickly grasp trends and produce accurate forecasts. For more details, refer to the Improve Forecast Accuracy with TimeGPT tutorial.
What is fine-tuning?
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.
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?
Do I have to fine-tune every series?
No, you do not need to fine-tune every series individually. When using the finetune_steps
parameter, the model fine-tunes across all series in your dataset simultaneously. This cross-learning approach allows the model to learn from multiple series at once, which can improve individual forecasts.
Selecting the right number of fine-tuning steps may require experimentation. As fine-tuning steps increase, the model becomes more specialized to your dataset but takes longer to train and may become more prone to overfitting.
Can I save fine-tuned parameters?
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.
Need more help? Contact our support team.