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>.
| 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 |
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