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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 |
| Reference manual: | transformerForecasting.pdf |
| Vignettes: |
user_guide (source, R code) |
| Package source: | transformerForecasting_0.1.0.tar.gz |
| Windows binaries: | r-devel: not available, r-release: transformerForecasting_0.1.0.zip, r-oldrel: not available |
| macOS binaries: | r-devel (arm64): transformerForecasting_0.1.0.tgz, r-release (arm64): transformerForecasting_0.1.0.tgz, r-oldrel (arm64): transformerForecasting_0.1.0.tgz, r-devel (x86_64): transformerForecasting_0.1.0.tgz, r-release (x86_64): transformerForecasting_0.1.0.tgz, r-oldrel (x86_64): transformerForecasting_0.1.0.tgz |
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These binaries (installable software) and packages are in development.
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