Long Short-Term Memory Networks

Also known as LSTM

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Overview

LSTMs process sequences step by step, using input, forget, and output gates to control what information enters, persists in, and leaves a memory state. Researchers apply them to time-series forecasting, text and speech tasks, sensor-based prediction, and control, often comparing them with simpler statistical models, convolutional networks, Transformers, and state-space models. Common extensions include bidirectional and attention-based LSTMs, ensembles, hybrid architectures, and methods that add physical constraints or estimate uncertainty. LSTMs remain useful for many sequence tasks, but their accuracy depends on the data and evaluation setting; limited parallelism and difficulty learning long-range dependencies compete with their simplicity and effectiveness on moderate-length sequences.

Latest papers

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41 papers7 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

  1. A Comparison of Traditional Machine Learning Algorithms and LSTM-Based Deep Learning Models for Email Sentiment Analysis

    May 5, 2026Virdio Samuel Saragih, Baruna Abirawa, Kartini Lovian Simbolon +3Sentiment AnalysisText Classification

    Faculty of Science Sumatra Institute of Technology

  2. Sentiment Analysis of Mobile Legends App Reviews Using Machine Learning and LSTM-Based Deep Learning Models

    May 2, 2026Vira Putri Maharani, Kharisa Harvanny, Daris Samudra +3Sentiment AnalysisLong Short-Term Memory Networks

    Department of Data Science Institut Teknologi Sumatera Lampung Selatan, Indonesia

  3. Remaining Useful Life Estimation for Turbofan Engines: A Comparative Study of Classical, CNN, and LSTM Approaches

    Apr 29, 2026Astitva Goel, Samarth Galchar, Sumit KanuRUL EstimationPredictive Maintenance

    Khoury College of Computer Science, Northeastern University

  4. Comparative Analysis of AutoML and BiLSTM Models for Cyberbullying Detection on Indonesian Instagram Comments

    Apr 29, 2026Raihana Adelia Putri, Aisyah Musfirah, Anggi Puspita Ningrum +3Long Short-Term Memory NetworksAttention Mechanisms

    Institut Teknologi Sumatera

  5. Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics

    Apr 21, 2026Syed Sajid Ullah, Amir KhanConstruction SafetyLong Short-Term Memory Networks

  6. Beyond Weather Correlation: A Comparative Study of Static and Temporal Neural Architectures for Fine-Grained Residential Energy Consumption Forecasting in Melbourne, Australia

    Apr 14, 2026Prasad Nimantha Madusanka Ukwatta Hewage, Hao WuElectricity Load ForecastingTime Series Forecasting

    School of Computing and Information Technology Victoria University, Sydney, NSW, Australia

  7. Variational LSTM with Augmented Inputs: Nonlinear Response History Metamodeling with Aleatoric and Epistemic Uncertainty

    Apr 2, 2026Manisha Sapkota, Min Li, Bowei LiSurrogate ModelingUncertainty Propagation

    Department of Civil, Environmental, and Construction Engineering, Texas Tech University, 2500 Broadway St, Lubbock 79409, TX, USA · Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 110 8th Street, Troy 12180, NY, USA · National Wind Institute, Texas Tech University, 1009 Canton Ave, Lubbock 79409, TX, USA

  8. Consistency-Driven Dual LSTM Models for Kinematic Control of a Wearable Soft Robotic Arm

    Mar 18, 2026Xingyu Chen, Yi Xiong, Li WenCycle ConsistencyRobot Learning

    School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China

  9. Wideband Power Amplifier Behavioral Modeling Using an Amplitude Conditioned LSTM

    Feb 17, 2026Abdelrahman Abdelsalam, You FeiNonlinear System IdentificationLong Short-Term Memory Networks

    School of Electronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

  10. KAN vs LSTM Performance in Time Series Forecasting

    Nov 23, 2025Tabish Ali Rather, S M Mahmudul Hasan Joy, Nadezda Sukhorukova +1Time Series ForecastingNon-Stationary Time Series Forecasting

    Department of Mathematics, Swinburne University of Technology, Hawthorn, 3122, Victoria, Australia.

  11. Detecting Heel Strike and toe off Events Using Kinematic Methods and LSTM Models

    Mar 2, 2025Longbin Zhang, Zhizhang Li, Xinyi Fu +11Gait AnalysisLong Short-Term Memory Networks

    School of Artificial Intelligence and Robotics, Hunan University, Changsha, China · School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore · Fourth Hospital of Changsha, Hunan Normal University, Changsha, China +2