We are happy to announce the release of nixtlar version 0.6.2, introducing support for TimeGEN-1, TimeGPT optimized for Azure.

Key updates include:

• Azure Integration: You can now use TimeGEN-1, a version of TimeGPT optimized for the Azure infrastructure, directly through nixtlar. Simply configure your API key and Base URL to get started. For setup instructions, please check out our Azure Quickstart vignette.
• Enhanced Date Support: In response to user feedback, we’ve improved support for date objects created with the as.Date function. For optimal performance, nixtlar now requires dates in the format YYYY-MM-DD or YYYY-MM-DD hh:mm:ss, either as characters or date-objects, and this update resolves issues with the latter format.
• Business-Day Frequency Inference: nixtlar now supports inferring business-day frequency, which users previously had to specify directly.
• Bug Fixes: This version also includes fixes for minor bugs reported by our users, ensuring overall stability and performance.

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.

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

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")

library(nixtlar)

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

nixtla_set_api_key(api_key = "Your API key here")

2. 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

3. 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 45.19045      30.49691      35.50842
#> 2        BE 2016-12-31 01:00:00 43.24445      28.96423      35.37463
#> 3        BE 2016-12-31 02:00:00 41.95839      27.06667      35.34079
#> 4        BE 2016-12-31 03:00:00 39.79649      27.96751      32.32625
#> 5        BE 2016-12-31 04:00:00 39.20454      24.66072      30.99895
#> 6        BE 2016-12-31 05:00:00 40.10878      23.05056      32.43504
#>   TimeGPT-hi-80 TimeGPT-hi-95
#> 1      54.87248      59.88399
#> 2      51.11427      57.52467
#> 3      48.57599      56.85011
#> 4      47.26672      51.62546
#> 5      47.41012      53.74836
#> 6      47.78252      57.16700

Optionally, plot the results

nixtla_client_plot(df, nixtla_client_fcst, max_insample_length = 200)

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 56.07623     -28.58337
#> 2        BE 2016-10-27 01:00:00 44.86   FALSE 52.41973     -32.23986
#> 3        BE 2016-10-27 02:00:00 42.31   FALSE 52.81474     -31.84486
#> 4        BE 2016-10-27 03:00:00 39.66   FALSE 52.59026     -32.06934
#> 5        BE 2016-10-27 04:00:00 38.98   FALSE 52.67297     -31.98662
#> 6        BE 2016-10-27 05:00:00 42.31   FALSE 54.10659     -30.55301
#>   TimeGPT-hi-99
#> 1      140.7358
#> 2      137.0793
#> 3      137.4743
#> 4      137.2499
#> 5      137.3326
#> 6      138.7662

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

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.

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:

• Get started and set up your API key
• Do anomaly detection
• Perform time series cross-validation
• Use exogenous variables
• Generate historical forecasts

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

Are you a Python user? If yes, then check out the Python SDK for TimeGPT. You can also refer to our API reference for support in other programming languages.

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

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

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.