Transformer-based deep learning architecture for time series forecasting[Formula presented]

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dc.contributor.authorNayak G.H.H.
dc.contributor.authorAlam M.W.
dc.contributor.authorAvinash G.
dc.contributor.authorKumar R.R.
dc.contributor.authorRay M.
dc.contributor.authorBarman S.
dc.contributor.authorSingh K.N.
dc.contributor.authorNaik B.S.
dc.contributor.authorAlam N.M.
dc.contributor.authorPal P.
dc.contributor.authorRathod S.
dc.contributor.authorBisen J.
dc.date.accessioned2025-01-13T07:03:15Z
dc.date.available2025-01-13T07:03:15Z
dc.date.issued2024
dc.description.abstractTime series forecasting faces challenges due to the non-stationarity, nonlinearity, and chaotic nature of the data. Traditional deep learning models like RNNs, LSTMs, and GRUs 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. � 2024
dc.identifier.doi10.1016/j.simpa.2024.100716
dc.identifier.issn26659638
dc.identifier.urihttps://dl.bhu.ac.in/ir/handle/123456789/1015
dc.language.isoen
dc.publisherElsevier B.V.
dc.subjectDeep learning
dc.subjectTime series forecasting
dc.subjectTransformer
dc.titleTransformer-based deep learning architecture for time series forecasting[Formula presented]
dc.typeArticle
journal.titleSoftware Impacts
journalvolume.identifier.volume22

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