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  "Description": "Time series forecasting faces challenges due to the\nnon-stationarity, nonlinearity, and chaotic nature of the data.\nTraditional deep learning models like Recurrent Neural Network\n(RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit\n(GRU) process data sequentially but are inefficient for long\nsequences. To overcome the limitations of these models, we\nproposed a transformer-based deep learning architecture\nutilizing an attention mechanism for parallel processing,\nenhancing prediction accuracy and efficiency. This paper\npresents user-friendly code for the implementation of the\nproposed transformer-based deep learning architecture utilizing\nan attention mechanism for parallel processing. References:\nNayak et al. (2024) <doi:10.1007/s40808-023-01944-7> and Nayak\net al. (2024) <doi:10.1016/j.simpa.2024.100716>.",
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