Package: nixtlar 1.0.0

Mariana Menchero

nixtlar: A Software Development Kit for 'Nixtla''s 'TimeGPT'

A Software Development Kit for working with 'Nixtla''s 'TimeGPT', a foundation model for time series forecasting. 'API' is an acronym for 'application programming interface'; this package allows users to interact with 'TimeGPT' via the 'API'. You can set and validate 'API' keys and generate forecasts via 'API' calls. It is compatible with 'tsibble' and base R. For more details visit <https://www.nixtla.io/docs>.

Authors:Mariana Menchero [aut, cre], Nixtla [cph]

nixtlar_1.0.0.tar.gz
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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
nixtlar/json (API)

# Install 'nixtlar' in R:
install.packages('nixtlar', repos = c('https://nixtla.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nixtla/nixtlar/issues

Pkgdown/docs site:https://nixtla.github.io

Datasets:

On CRAN:

Conda:

8.15 score 40 stars 1 packages 84 scripts 652 downloads 17 exports 37 dependencies

Last updated from:fc25b89a4e. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK184
source / vignettesOK206
linux-release-x86_64OK189
macos-release-arm64OK101
macos-oldrel-arm64OK85
windows-develOK96
windows-releaseOK106
windows-oldrelOK110
wasm-releaseOK137

Exports:.generate_output_dates.get_client_steup.get_client_version.get_model_params.level_from_quantiles.r_frequency.transient_errors.validate_exogenousinfer_frequencynixtla_client_cross_validationnixtla_client_detect_anomaliesnixtla_client_forecastnixtla_client_historicnixtla_client_plotnixtla_client_setupnixtla_set_api_keynixtla_validate_api_key

Dependencies:askpassclicpp11curldplyrfarvergenericsggplot2gluegtablehttr2isobandlabelinglifecyclelubridatemagrittropensslpillarpkgconfigpurrrR6rappdirsRColorBrewerrlangS7scalesstringistringrsystibbletidyrtidyselecttimechangeutf8vctrsviridisLitewithr

Data Requirements
1. Input Requirements | 2. Multiple Series | 3. Exogenous Variables | 4. Missing values | 5. Minimum data requirements
Rendered fromdata-requirements.Rmdusingknitr::rmarkdown

Last update: 2026-06-23
Started: 2024-07-07

Get Started
1. Setting up your API key | a. Using the nixtla_client_setup function | b. Using an environment variable | Validate your API key | 2. Generate TimeGPT forecast | 3. Plot TimeGPT forecast
Rendered fromget-started.Rmdusingknitr::rmarkdown

Last update: 2026-06-23
Started: 2023-12-27

Setting Up Your API Key
1. What is an API key? | 2. How can I get one? | 3. How do I set up my API key? | 3.1 Using the nixtlar::nixtla_client_setup function | 3.2 Using an environment variable | a. Using options | b. Using .Renviron | 4. Validate your API key (optional) | 5. Azure endpoints
Rendered fromsetting-up-your-api-key.Rmdusingknitr::rmarkdown

Last update: 2026-06-23
Started: 2024-07-07

Exogenous Variables
1. Exogenous variables | 2. Load data | 3a. Forecasting electricity prices using historic and future exogenous variables | 3b. Forecasting electricity prices using only historic exogenous variables | 3c. Forecasting future exogenous variables | 3d. Forecasting electricity prices using both future and historic exogenous variables | 4. Plot TimeGPT forecast
Rendered fromexogenous-variables.Rmdusingknitr::rmarkdown

Last update: 2025-02-17
Started: 2023-12-05

TimeGEN-1 Quickstart (Azure)
1. Set up a TimeGEN-1 endpoint account and generate your API key on Azure. | 2. Install nixtlar | 3. Set up the Base URL and API key | 4. Start making forecasts! | Discover the power of TimeGEN on Azure via nixtlar.
Rendered fromazure-quickstart.Rmdusingknitr::rmarkdown

Last update: 2025-02-13
Started: 2024-10-25

Fine-tuning
1. Introduction | 2. Fine-tuning parameters | 3. Example | 4. Final recommendations
Rendered fromfine-tuning.Rmdusingknitr::rmarkdown

Last update: 2024-12-19
Started: 2024-12-19

VN1 Forecasting Competition
Introduction | TimeGPT 2nd Place Submission | Try It Yourself! | References
Rendered fromvn1-forecasting-competition.Rmdusingknitr::rmarkdown

Last update: 2024-12-13
Started: 2024-12-13

Anomaly Detection
1. Anomaly detection | 2. Load data | 3. Detect Anomalies | 4. Plot anomalies
Rendered fromanomaly-detection.Rmdusingknitr::rmarkdown

Last update: 2024-10-25
Started: 2023-12-13

Cross-Validation
1. Time series cross-validation | 2. Load data | 3. Perform time series cross-validation | 4. Plot cross-validation results
Rendered fromcross-validation.Rmdusingknitr::rmarkdown

Last update: 2024-10-25
Started: 2023-12-06

Historical Forecast
1. TimeGPT Historical Forecast | 2. Load data | 3. Forecast historical data | 3.1 Fitted values from nixtlar::nixtla_client_forecast
Rendered fromhistorical-forecast.Rmdusingknitr::rmarkdown

Last update: 2024-10-25
Started: 2023-12-28

Long-Horizon Forecasting
1. Long-horizon forecasting | 2. Load data | 3. Forecast with a long-horizon | 4. Plot the long-horizon forecast | 5. Evaluate the long-horizon model
Rendered fromlong-horizon.Rmdusingknitr::rmarkdown

Last update: 2024-10-25
Started: 2024-07-07

Prediction Intervals
1. Uncertainty quantification via prediction intervals | 2. Load data | 3. Forecast with prediction intervals | 4. Plot prediction intervals
Rendered fromprediction-intervals.Rmdusingknitr::rmarkdown

Last update: 2024-10-25
Started: 2024-07-07

Quantile Forecasts
1. Uncertainty quantification via quantiles | 2. Load data | 3. Forecast with quantiles | 4. Plot quantiles
Rendered fromquantiles.Rmdusingknitr::rmarkdown

Last update: 2024-10-25
Started: 2024-06-18

Special Topics
Special topics | 1. Handling missing values | 2. Specifying the frequency of your data
Rendered fromspecial-topics.Rmdusingknitr::rmarkdown

Last update: 2024-10-25
Started: 2024-07-07