RNN-LSTM: From applications to modeling techniques and beyond ...
Jun 1, 2024 · Long Short-Term Memory (LSTM) is a popular Recurrent Neural Network (RNN) algorithm known for its ability to effectively analyze and process sequential data with long-term dependencies. …
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 gating …
LSTM-ARIMA as a hybrid approach in algorithmic investment strategies
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 strategy. …
A survey on long short-term memory networks for time series prediction
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 time-variant …
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 memory cell, …
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…
Enhancing streamflow forecasting using an LSTM hybrid model with ...
Consequently, LSTM attracts considerable attention and has been rigorously validated in hydrological forecasting. Chen et al. (2020) compared an artificial neural network (ANN) with LSTM for daily …
PI-LSTM: Physics-informed long short-term memory ... - ScienceDirect
Oct 1, 2023 · The PI-LSTM network, inspired by and compared with existing physics-informed deep learning models (PhyCNN and PhyLSTM), was validated using the numerical simulation results of …
Singular Value Decomposition-based lightweight LSTM for time series ...
Long–short-term memory (LSTM) neural networks are known for their exceptional performance in various domains, particularly in handling time series dat…
Improving streamflow prediction in the WRF-Hydro model with LSTM ...
Feb 1, 2022 · In this approach, LSTM was employed to predict the residual errors of WRF-Hydro; in contrast, the conventional approach with LSTM predicts streamflow directly. Here, we performed …