Version 1.0.0 of nixtlar is now available on CRAN! (2026-06-22)

We are happy to announce the release of nixtlar version 1.0.0, our first major release.

Key updates include:

Historic and Future Exogenous Variables: The forecasting and cross-validation functions now let you distinguish between exogenous variables that have only historical values and those that include both historical and future values.
Updated API Endpoints: nixtlar now uses the GET method for the /model_params and /validate_api_key endpoints, and forwards a nixtla-model request header so you can work with different models.
Lighter Dependencies: We removed the unused .make_request helper and dropped the future and future.apply dependencies.
Bug Fixes: This version resolves several date-reconstruction issues and a problem when using future exogenous variables with h = 1.

Note that nixtlar now requires R (>= 4.1.0).

Thank you for your continued support and feedback, which help us make nixtlar better. We encourage you to update to the latest version to take advantage of these improvements.

TimeGPT-1

The first foundation model for time series forecasting and anomaly detection

TimeGPT is a production-ready, generative pretrained transformer for time series forecasting, developed by Nixtla. It is capable of accurately predicting various domains such as retail, electricity, finance, and IoT, with just a few lines of code. Additionally, it can detect anomalies in time series data.

TimeGPT was initially developed in Python but is now available to R users through the nixtlar package.

Installation
Forecast Using TimeGPT in 3 Easy Steps
Anomaly Detection Using TimeGPT in 3 Easy Steps
Features and Capabilities
Documentation
API Support
How to Cite
License
Get in Touch

Installation

nixtlar is available on CRAN, so you can install the latest stable version using install.packages.

# Install nixtlar from CRAN
install.packages("nixtlar")

# Then load it 
library(nixtlar)

Alternatively, you can install the development version of nixtlar from GitHub with devtools::install_github.

# install.packages("devtools")
devtools::install_github("Nixtla/nixtlar")

Forecast Using TimeGPT in 3 Easy Steps

library(nixtlar)

Set your API key. Get yours at nixtla.io/dashboard

nixtla_set_api_key(api_key = "Your API key here")

Load sample data

df <- nixtlar::electricity
head(df)
#>   unique_id                  ds     y
#> 1        BE 2016-10-22 00:00:00 70.00
#> 2        BE 2016-10-22 01:00:00 37.10
#> 3        BE 2016-10-22 02:00:00 37.10
#> 4        BE 2016-10-22 03:00:00 44.75
#> 5        BE 2016-10-22 04:00:00 37.10
#> 6        BE 2016-10-22 05:00:00 35.61

Forecast the next 8 steps ahead

nixtla_client_fcst <- nixtla_client_forecast(df, h = 8, level = c(80,95))
#> Frequency chosen: h
head(nixtla_client_fcst)
#>   unique_id                  ds  TimeGPT TimeGPT-lo-95 TimeGPT-lo-80
#> 1        BE 2016-12-31 00:00:00 45.19122      30.49719      35.50965
#> 2        BE 2016-12-31 01:00:00 43.24537      28.96447      35.37618
#> 3        BE 2016-12-31 02:00:00 41.95892      27.06669      35.34091
#> 4        BE 2016-12-31 03:00:00 39.79675      27.96763      32.32674
#> 5        BE 2016-12-31 04:00:00 39.20512      24.66191      31.00021
#> 6        BE 2016-12-31 05:00:00 40.10902      23.05225      32.43594
#>   TimeGPT-hi-80 TimeGPT-hi-95
#> 1      54.87278      59.88525
#> 2      51.11456      57.52628
#> 3      48.57694      56.85116
#> 4      47.26675      51.62587
#> 5      47.41004      53.74834
#> 6      47.78209      57.16578

Optionally, plot the results

nixtla_client_plot(df, nixtla_client_fcst, max_insample_length = 200)

Anomaly Detection Using TimeGPT in 3 Easy Steps

Do anomaly detection with TimeGPT, also in 3 easy steps! Follow steps 1 and 2 from the previous section and then use the nixtla_client_detect_anomalies and the nixtla_client_plot functions.

nixtla_client_anomalies <- nixtlar::nixtla_client_detect_anomalies(df) 
#> Frequency chosen: h
head(nixtla_client_anomalies)
#>   unique_id                  ds     y anomaly  TimeGPT TimeGPT-lo-99
#> 1        BE 2016-10-27 00:00:00 52.58   FALSE 56.07206     -28.58840
#> 2        BE 2016-10-27 01:00:00 44.86   FALSE 52.41392     -32.24654
#> 3        BE 2016-10-27 02:00:00 42.31   FALSE 52.80694     -31.85352
#> 4        BE 2016-10-27 03:00:00 39.66   FALSE 52.58330     -32.07716
#> 5        BE 2016-10-27 04:00:00 38.98   FALSE 52.66963     -31.99083
#> 6        BE 2016-10-27 05:00:00 42.31   FALSE 54.10218     -30.55829
#>   TimeGPT-hi-99
#> 1      140.7325
#> 2      137.0744
#> 3      137.4674
#> 4      137.2438
#> 5      137.3301
#> 6      138.7626

nixtlar::nixtla_client_plot(df, nixtla_client_anomalies, plot_anomalies = TRUE)

Features and Capabilities

nixtlar provides access to TimeGPT’s features and capabilities, such as:

Zero-shot Inference: TimeGPT can generate forecasts and detect anomalies straight out of the box, requiring no prior training data. This allows for immediate deployment and quick insights from any time series data.
Fine-tuning: Enhance TimeGPT’s capabilities by fine-tuning the model on your specific datasets, enabling the model to adapt to the nuances of your unique time series data and improving performance on tailored tasks.
Add Exogenous Variables: Incorporate additional variables that might influence your predictions to enhance forecast accuracy. (E.g. Special Dates, events or prices)
Multiple Series Forecasting: Simultaneously forecast multiple time series data, optimizing workflows and resources.
Custom Loss Function: Tailor the fine-tuning process with a custom loss function to meet specific performance metrics.
Cross Validation: Implement out of the box cross-validation techniques to ensure model robustness and generalizability.
Prediction Intervals: Provide intervals in your predictions to quantify uncertainty effectively.
Irregular Timestamps: Handle data with irregular timestamps, accommodating non-uniform interval series without preprocessing.

Documentation

For comprehensive documentation, please refer to our vignettes, which cover a wide range of topics to help you effectively use nixtlar. The current documentation includes guides on how to:

The documentation is an ongoing effort, and we are working on expanding its coverage.

Python SDK

Are you a Python user? If yes, then check out the Python SDK for TimeGPT.

How to Cite

If you find TimeGPT useful for your research, please consider citing the TimeGPT-1 paper. The associated reference is shown below.

Garza, A., Challu, C., & Mergenthaler-Canseco, M. (2024). TimeGPT-1. arXiv preprint arXiv:2310.03589. Available at https://arxiv.org/abs/2310.03589

License

TimeGPT is closed source. However, this SDK is open source and available under the Apache 2.0 License, so feel free to contribute!

Get in Touch

We welcome your input and contributions to the nixtlar package!

Report Issues: If you encounter a bug or have a suggestion to improve the package, please open an issue in GitHub.
Contribute: You can contribute by opening a pull request in our repository. Whether it is fixing a bug, adding a new feature, or improving the documentation, we appreciate your help in making nixtlar better.

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