Package: transformerForecasting 0.1.0

transformerForecasting: Transformer Deep Learning Model for Time Series Forecasting

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>.

Authors:G H Harish Nayak [aut, cre], Md Wasi Alam [ths], B Samuel Naik [ctb], G Avinash [ctb], Kabilan S [ctb], Varshini B S [ctb], Mrinmoy Ray [ths], Rajeev Ranjan Kumar [ths]

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transformerForecasting.pdf |transformerForecasting.html
transformerForecasting/json (API)

# Install 'transformerForecasting' in R:
install.packages('transformerForecasting', repos = c('https://harish11999.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.00 score 1 stars 3 exports 51 dependencies

Last updated 12 days agofrom:1b61751eb1. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 08 2025
R-4.5-winOKMar 08 2025
R-4.5-macOKMar 08 2025
R-4.5-linuxOKMar 08 2025
R-4.4-winOKMar 08 2025
R-4.4-macOKMar 08 2025
R-4.4-linuxOKMar 08 2025
R-4.3-winOKMar 08 2025
R-4.3-macOKMar 08 2025

Exports:%>%install_r_dependenciesTRANSFORMER

Dependencies:backportsbase64encclicolorspaceconfigfansifarvergenericsggplot2gluegtablehereisobandjsonlitekeraslabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigpngprocessxpsR6rappdirsRColorBrewerRcppRcppTOMLreticulaterlangrprojrootrstudioapiscalestensorflowtfautographtfrunstibbletidyselectutf8vctrsviridisLitewhiskerwithryamlzeallot

transformerForecasting R package

Rendered fromuser_guide.Rmdusingknitr::rmarkdownon Mar 08 2025.

Last update: 2025-03-07
Started: 2025-03-07