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>.
Version: 0.1.0 Depends: R (≥ 4.0.0) Imports: ggplot2, keras, tensorflow, magrittr, reticulate (≥ 1.20) Suggests: dplyr, knitr, lubridate, readr, rmarkdown, utils Published: 2025-03-07 DOI: 10.32614/CRAN.package.transformerForecasting Author: 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] Maintainer: G H Harish Nayak <harishnayak626 at gmail.com> License: GPL-3 NeedsCompilation: no CRAN checks: transformerForecasting results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=transformerForecasting to link to this page.
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