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 …
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 …
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 …
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 …
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 …
Forecasting and Anomaly Detection approaches using LSTM and …
Apr 1, 2021 · Recently, LSTM emerges as a powerful technique to learn the long-term dependencies and represent the relationship between current events and previous events …
Short-Term Load Forecasts Using LSTM Networks - ScienceDirect
Feb 1, 2019 · With the increasing load requirements and the sophistication of power stations, knowing in advance about the electrical load not only at short-term pe…
Advanced fusion of MTM-LSTM and MLP models for time series …
Jun 1, 2024 · LSTM models can be used for regression issues, inhabitant data on the target rate, and the effect on the output [8]. The network of the hidden layer (also known as multilayer …
Optimizing LSTM with multi-strategy improved WOA for robust …
Jan 1, 2024 · LSTM networks are popular for predicting data with nonlinear and temporal properties. However, it is difficult to select optimal hyperparameters using…