Recurrent Neural Networks and its Applications in Time Series Data
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
CRC Press
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Recurrent 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.
Açıklama
Anahtar Kelimeler
Pattern Recognition, Time Series Analysis, Its Applications, Neural Network Application, Neural-Networks, Recurrent Neural Network Model, Sequencing Analysis, Sequential Data, Short Term Memory, Time-Series Analysis, Time-Series Data, Times Series, Long Short-Term Memory
Kaynak
A to Z of Deep Learning and AI
WoS Q Değeri
Scopus Q Değeri
N/A
