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 …
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. …
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 practitioners, …
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 …
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 …
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 captures …
Performance analysis of neural network architectures for time series ...
Dec 1, 2025 · LSTM-based hybrid architectures, particularly LSTM-RNN and LSTM-GRU configurations, demonstrate reliable performance across multiple domains and should be considered as primary …
Deep learning modeling in electricity load forecasting: Improved ...
Dec 1, 2024 · Comparative analysis of the implemented methods confirms the superior accuracy of DWT-LSTM, followed by LSTM, NARX, and SVM methods, respectively. Our proposed forecasting …
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 detailed overview on …
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…