The working and intuition behind the LSTM and LSTM Autoencoder can be found from the reference link ... if the MAE becomes greater than the Threshold, we can detect anomaly correspond to that Date, or ...

This repository contains the implementation of an LSTM (Long Short-Term Memory) Autoencoder for detecting anomalies in time series data. You can either use a pre-trained model provided here or train a ...

We propose a hybrid deep-learning model that combines long short-term memory (LSTM) with an autoencoder (AE) for anomaly detection tasks in IAQ to address this issue. In our approach, the LSTM network ...

Popular deep learning architectures that can be used in an anomaly detection framework include: Autoencoder ... Recurrent Neural Network (e.g. LSTM, GRU, Attention), however, can handle temporal ...