Nazza, Muhannad AkramAl-Okbi, Nada KhalilŞahin, Canan BaturHu, GangSumari, PutraSnasel, VaclavAbualigah, Laith2026-06-192026-06-192025978-104045106-9978-103283483-2https://doi.org/10.1201/9781003516385-8https://hdl.handle.net/20.500.12899/4901Recurrent Neural Networks (RNNs) have been extensively embraced for sequencing analysis and sequential data modelling, especially time series data. This paper delves into the principles and applications of the RNNs concentrically time series analysis. The advantages of time series RNNs, including long short-term memory (LSTM) and Gated recurrent units (GRU), are discussed in detail. Different RNN models have been implemented on both synthetic and real-look time series datasets to assess their performance. The results clearly show RNNs outperformed traditional forecasting and pattern recognition methods. Lastly, the issues related to RNN training, namely, vanishing gradients and overfitting, are briefly addressed, and potential improvements for RNNs are outlined. © 2025 Laith Abualigah.eninfo:eu-repo/semantics/closedAccessPattern RecognitionTime Series AnalysisIts ApplicationsNeural Network ApplicationNeural-NetworksRecurrent Neural Network ModelSequencing AnalysisSequential DataShort Term MemoryTime-Series AnalysisTime-Series DataTimes SeriesLong Short-Term MemoryBook Chapter10.1201/9781003516385-857642-s2.0-105013378080N/A