We are excited to announce the release of nixtlar
version 0.6.0, which integrates the latest
release of the TimeGPT
API—v2. This update focuses on
what matters most to our users: speed, scalability, and reliability.
Key updates include:
Data Structures: nixtlar
now
extends support to tibbles
, in addition to the previously
supported data frames and tsibbles. This broadens the range of data
structures that can be used in your workflows.
Date Formats: For efficiency,
nixtlar
now strictly requires dates to be in the format
YYYY-MM-DD
or YYYY-MM-DD hh:mm:ss
, either as
character strings or date-time objects. For more details, please refer
to our Get
Started guide and Data
Requirements vignette.
Default ID Column: In alignment with the Python
SDK, nixtlar
now defaults the id_col
to
unique_id
. This means you no longer need to specify this
column if it is already named unique_id
. If your dataset
contains only one series, simply set id_col=NULL
. The
id_col
only accepts characters or integers.
These changes leverage the capabilities of TimeGPT
’s new
API and align nixtlar
more closely with the Python SDK,
ensuring a better user experience.
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.
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")
::install_github("Nixtla/nixtlar") devtools
library(nixtlar)
nixtla_set_api_key(api_key = "Your API key here")
<- nixtlar::electricity
df 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
<- nixtla_client_forecast(df, h = 8, level = c(80,95))
nixtla_client_fcst #> 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.
<- nixtlar::nixtla_client_detect_anomalies(df)
nixtla_client_anomalies #> 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
::nixtla_client_plot(df, nixtla_client_anomalies, plot_anomalies = TRUE) nixtlar
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:
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.