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>.