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transformerForecasting  

Transformer Deep Learning Model for Time Series Forecasting
View on CRAN: Click here


Download and install transformerForecasting package within the R console
Install from CRAN:
install.packages("transformerForecasting")

Install from Github:
library("remotes")
install_github("cran/transformerForecasting")

Install by package version:
library("remotes")
install_version("transformerForecasting", "0.1.0")



Attach the package and use:
library("transformerForecasting")
Maintained by
G H Harish Nayak
[Scholar Profile | Author Map]
First Published: 2025-03-07
Latest Update: 2025-03-07
Description:
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>.
How to cite:
G H Harish Nayak (2025). transformerForecasting: Transformer Deep Learning Model for Time Series Forecasting. R package version 0.1.0, https://cran.r-project.org/web/packages/transformerForecasting. Accessed 30 Apr. 2025.
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