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

Anomaly Detections and Network Intrusion Detection, and Complexity Scoring.

Deep Learning based EEG forecasting toolbox

Anomaly detection for Sequential dataset

CobamasSensorOD is a framework used to create, train and visualize an autoencoder on sequential multivariate data.

Develop LSTM Autoencoder model, to detect anomaly in S&P 500 Index dataset.

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

Implementation of LSTM and LSTM-AE (Pytorch)

Multi-Patching for effective & powerful time-series classification

The project is to show how to use LSTM autoencoder and Azure Machine Learning SDK to detect anomalies in time series data

Analyze stock data for price forecasting and anomaly detection

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.