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 …
RNN-LSTM: From applications to modeling techniques and …
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 sequentia…
Long Short-Term Memory Network - an overview - ScienceDirect
Jul 7, 2020 · A Long Short-Term Memory Network, also known as LSTM, is an advanced recurrent neural network that uses "gates" to capture both long-term and short-term memory. …
Working Memory Connections for LSTM - ScienceDirect
Dec 1, 2021 · For the sMNIST task, peephole LSTM performs slightly better than vanilla LSTM. LSTM with Working Memory Connections, instead, outperforms the competing architectures in …
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 …
Physics-informed multi-LSTM networks for metamodeling of …
Sep 1, 2020 · This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data. The ba…
Fundamentals of Recurrent Neural Network (RNN) and Long Short …
Mar 1, 2020 · All major open source machine learning frameworks offer efficient, production-ready implementations of a number of RNN and LSTM network architectures. Naturally, some …
Improving streamflow prediction in the WRF-Hydro model with …
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 …
LSTM-FKAN coupled with feature extraction technique for …
May 1, 2025 · The soil characteristic data is represented by root zone soil moisture, which is derived from raster data. The LSTM-FKAN coupled with feature extraction technique …
NOA-LSTM: An efficient LSTM cell architecture for time series ...
Mar 15, 2024 · The LSTM architecture has been criticized for being ad-hoc and having many variable components whose contributions are not evident. Consequently, it is uncertain …