There are 4 repositories under lstm-autoencoder topic.
PyTorch Dual-Attention LSTM-Autoencoder For Multivariate Time Series
This research project will illustrate the use of machine learning and deep learning for predictive analysis in industry 4.0.
University Project for Anomaly Detection on Time Series data
video summarization lstm-gan pytorch implementation
Anamoly Detection in Time Series data of S&P 500 Stock Price index (of top 500 US companies) using Keras and Tensorflow
A Novel Approach leveraging Auto-Encoders, LSTM Networks and Maximum Entropy Principle for Video Super-Resolution (Upscaling and Frame Interpolation)
Final Project for the 'Machine Learning and Deep Learning' Course at AGH Doctoral School
Sparse Residual LSTM Autoencoder | Robust Autoencoder for Anomaly Detection in ECG | 2024 대한전자공학회 추계학술대회 | Autumn Annual Conference of IEIE, 2024 | OMS 2
Anomaly detection for Sequential dataset
Deep Learning based EEG forecasting toolbox
Detect Anomalies with Autoencoders in Time Series data
Time Series Forecasting using RNN, Anomaly Detection using LSTM Auto-Encoder and Compression using Convolutional Auto-Encoder
CobamasSensorOD is a framework used to create, train and visualize an autoencoder on sequential multivariate data.
Anomaly Detections and Network Intrusion Detection, and Complexity Scoring.
Develop LSTM Autoencoder model, to detect anomaly in S&P 500 Index dataset.
Multi-Patching for effective & powerful time-series classification
This project focuses on developing and deploying a robust system for detecting and classifying Multi-Class Distributed Denial of Service (DDoS) attacks.
Stock Market Manipulation with Deep Learning. Explore code, datasets, and architectures for detecting and understanding manipulation in financial markets. Join us in researching fair and transparent markets.
High Volatility Stock Prediction using Long Short-term Memory (LSTM)
Generating short length description of news articles
The project is to show how to use LSTM autoencoder and Azure Machine Learning SDK to detect anomalies in time series data
Implementing machine learning pipeline for anomaly detection
LSTM model for time-series forecasting. LSTM autoencoder for time-series anomaly detection. Convolutional neural network for time-series autoencoding.
Detecting conspicuous electrocardiograms (ECG) using LSTM Autoencoder. MLflow is utilized for experiment tracking.
This is the technical task by Eilink Digital Research Lab.
Seismic Activity Detection Algorithm Based On LSTM Autoencoder (NASA International Space Apps Challenge Cleveland 3rd place Project)
A complete workflow for building, training, and deploying a lightweight LSTM Autoencoder anomaly detector for temperature data on the ESP32 microcontroller—without TensorFlow or TFLite. This project uses PyTorch, ONNX, and C++ for efficient, real-time anomaly detection on edge devices.