RNN-LSTM: From applications to modeling techniques and …
Jun 1, 2024 · LSTM has been specifically designed to address the issue of vanishing gradients, which makes vanilla RNNs unsuitable for learning long-term dependencies (Jaydip and Sidra, …
Long Short-Term Memory Network - an overview - ScienceDirect
Network LSTM refers to a type of Long Short-Term Memory (LSTM) network architecture that is particularly effective for learning from sequences of data, utilizing specialized structures and …
Long Short-Term Memory - an overview | ScienceDirect Topics
LSTM, or long short-term memory, is defined as a type of recurrent neural network (RNN) that utilizes a loop structure to process sequential data and retain long-term information through a …
A survey on long short-term memory networks for time series …
Jan 1, 2021 · Recurrent neural networks and exceedingly Long short-term memory (LSTM) have been investigated intensively in recent years due to their ability to model and predict nonlinear …
Fundamentals of Recurrent Neural Network (RNN) and Long Short …
Mar 1, 2020 · Because of their effectiveness in broad practical applications, LSTM networks have received a wealth of coverage in scientific journals, technical blo…
LSTM-ARIMA as a hybrid approach in algorithmic investment …
Jun 23, 2025 · This study makes a significant contribution to the growing field of hybrid financial forecasting models by integrating LSTM and ARIMA into a novel algorithmic investment …
Performance analysis of neural network architectures for time …
Dec 1, 2025 · LSTM-based hybrid architectures, particularly LSTM-RNN and LSTM-GRU configurations, demonstrate reliable performance across multiple domains and should be …
A survey on anomaly detection for technical systems using LSTM …
Oct 1, 2021 · However, due to the recent emergence of different LSTM approaches that are widely used for different anomaly detection purposes, the present paper aims to present a …
Load forecasting method based on CNN and extended LSTM
Dec 1, 2024 · In this paper, we proposed a hybrid model utilizing CNN and dilated LSTM. The CNN effectively extracts comprehensive features from the load data, while the extended LSTM …
Working Memory Connections for LSTM - ScienceDirect
Dec 1, 2021 · In our experiments, we show that an LSTM equipped with Working Memory Connections achieves better results than comparable architectures, thus reflecting the …