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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "transformerForecasting" in publications use:'
type: software
license: GPL-3.0-only
title: 'transformerForecasting: Transformer Deep Learning Model for Time Series Forecasting'
version: 0.1.0
doi: 10.32614/CRAN.package.transformerForecasting
abstract: '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) <https://doi.org/10.1007/s40808-023-01944-7>
  and Nayak et al. (2024) <https://doi.org/10.1016/j.simpa.2024.100716>.'
authors:
- family-names: Harish Nayak
  given-names: G H
  email: harishnayak626@gmail.com
repository: https://harish11999.r-universe.dev
commit: 1b61751eb1131e422dd8766ec78466f283a295fb
date-released: '2025-03-07'
contact:
- family-names: Harish Nayak
  given-names: G H
  email: harishnayak626@gmail.com