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 …
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, …
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 …
Singular Value Decomposition-based lightweight LSTM for time …
Long–short-term memory (LSTM) neural networks are known for their exceptional performance in various domains, particularly in handling time series dat…
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 …
LSTM-based graph attention network for vehicle trajectory …
Jun 1, 2024 · However, many methods ignore the interaction among vehicles, which results in limited accuracy of prediction results. Therefore, we propose a Long Short-Term Memory …
DA-LSTM: A dynamic drift-adaptive learning framework for …
Aug 1, 2023 · In this paper, we build an interval load forecasting learning framework based on dynamic drift adaptation for LSTM networks, namely DA-LSTM. We design different load …
DB-LSTM: Densely-connected Bi-directional LSTM for human …
Jul 15, 2021 · A densely-connected Bi-directional LSTM (DB-LSTM) network is novelly proposed to capture the long-range temporal pattern in forward and backward directions, which …
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 …
LSTM, WaveNet, and 2D CNN for nonlinear time history prediction …
Jul 1, 2023 · LSTM has been previously developed and is utilized to serve as a reference model, while WaveNet and 2D CNN (i.e., it deals with the data in coupled time–frequency dimensions) …