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 …
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…
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. …
Predicting stock market index using LSTM - ScienceDirect
Sep 15, 2022 · The rapid advancement in artificial intelligence and machine learning techniques, availability of large-scale data, and increased computational capabi…
Lstm - an overview | ScienceDirect Topics
Aug 31, 2018 · LSTM, or Long Short-Term Memory networks, is defined as a type of neural network that extends Recurrent Neural Networks (RNN) to handle long-term dependencies by considering …
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 …
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 …
An interpretable hybrid deep learning model for flood forecasting …
Aug 1, 2024 · We propose an interpretable flood forecasting hybrid model based on Transformer, LSTM, and Adaptive Random Search Algorithm (AGRS), termed as AGRS-LSTM-Transformer. Investigating …