R Observatory: Metrics for transformerForecasting version 0.1.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

transformerForecasting

0.1.0Package record

Transformer Deep Learning Model for Time Series Forecasting

0packages depend

2Kdownloads / year

–test coverage

13/13flavors OK

Overview

About

Maintained by G H Harish NayakCRAN page ↗

Time series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) process data sequentially but are inefficient for long sequences. To overcome the limitations of these models, we proposed a transformer-based deep learning architecture utilizing an attention mechanism for parallel processing, enhancing prediction accuracy and efficiency. This paper presents user-friendly code for the implementation of the proposed transformer-based deep learning architecture utilizing an attention mechanism for parallel processing. References: Nayak et al. (2024) doi:10.1007/s40808-023-01944-7 and Nayak et al. (2024) doi:10.1016/j.simpa.2024.100716.

Install

R (CRAN)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("transformerForecasting", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-transformerforecasting

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-transformerForecasting

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-transformerForecasting

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 1.8 min · r-oldrel-windows-x86_64

Status changes

OK13 OK2026-08-04
NOTE12 OK, 1 NOTE2026-08-01
OK13 OK2026-06-09
ERROR12 OK, 1 ERROR2026-06-08
OK14 OK2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.1.0

READMENoVignettesYes · 1 · dynamicNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION

Help pages: 4 (1 internal)
Examples that run: 50%1 run in a plain check · 1 never run
Exported functions with examples: 67%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 0%

Vignettes (1)

user_guide HTML

How these are measured

Downloads

2K

CRAN downloads in the past year

Rank #23,323 · ~6/day · ~170/mo

Releases R versions

Daily download trend is not available in this view yet.

22130 days

55290 days

2K1 year

Compare downloads with other packages →

Also on68 r2u1 autocran

How downloads are counted

Dependencies

Declared dependencies

10 external dependencies (excludes base and recommended)

Installing transformerForecasting adds 41 packages (20 with compiled code, 25 MB of source tarballs). Heaviest: ggplot2 6.0 MB, keras 3.4 MB, tfruns 1.9 MB.

Depends (1)

R >= 4.0.0

Imports (5)

ggplot2 keras tensorflow magrittr reticulate

LinkingTo (0)

none

Suggests (6)

dplyr knitr lubridate readr rmarkdownutils

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

Datasets

People & History

People (8)

G H Harish Nayak

Author, Maintainer since 0.1.0 (2025)

Contributor

Contributor

Contributor

Contributor

Thesis advisor

Thesis advisor

Thesis advisor

Releases (1)

R
R 4.6.0 released · 2026-04-24
R
R 4.5.0 released · 2025-04-11
0.1.0Latest
record
2025-03-07 · current release
R
R 4.4.0 released · 2024-04-24

Package metadata

First published: 2025-03-07
Total releases: 1 / 1 yrs
License: GPL-3 OSI
Minimum R: ≥ 4.0.0
Bundled data: 6.8 KB / 1 file
Download size: 69 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("transformerForecasting")

Format

Harish Nayak, G. H., B Samuel Naik, G Avinash, Md Wasi Alam, Mrinmoy Ray, Rajeev Ranjan Kumar, Varshini B S, & S, K. (2025). transformerForecasting: Transformer Deep Learning Model for Time Series Forecasting (Version 0.1.0) [Computer software]. https://doi.org/10.32614/CRAN.package.transformerForecasting

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for transformerForecasting version 0.1.0 [Data set]. HJJB, LLC. Data release v2026-10-21. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-21, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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