{ "_id": "6a200cd2b401979e73447b70", "Package": "transformerForecasting", "Type": "Package", "Title": "Transformer Deep Learning Model for Time Series Forecasting", "Version": "0.1.0", "Authors@R": "c(person(\"G H\", \"Harish Nayak\", role = c(\"aut\", \"cre\"), email = \"harishnayak626@gmail.com\"), person(\"Md Wasi Alam\", role = c(\"ths\"), email = \"mw.Alam@icar.gov.in\"), person(\"B Samuel Naik\", role = c(\"ctb\"), email = \"banavathsamuelnaik@gmail.com\"), person(\"G Avinash\", role = c(\"ctb\"), email = \"avinash143stat@gmail.com\"), person(\"Kabilan\", \"S\", role = c(\"ctb\"), email = \"kabilan151414@gmail.com\"), person(\"Varshini B S\", role = c(\"ctb\"), email = \"varshinibs29@gmail.com\"), person(\"Mrinmoy Ray\", role = c(\"ths\"), email = \"mrinmoy4848@gmail.com\"),person(\"Rajeev Ranjan Kumar\", role = c(\"ths\"), email = \"rrk.uasd@gmail.com\"))", "Maintainer": "G H Harish Nayak ", "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. 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