The reconstruction loss of the trained LSTM Autoencoder model is estimated for the up-to-date reliability streaming data, and the result is used to infer MEC services’ runtime reliability anomalies.

In this study, we propose a long short-term memory autoencoder (LSTM-AE) based approach for anomaly identification in multivariate time-series measurement data of PTs. Generally, the LSTM-AE is ...

Time Series Anomaly Detection using Network KPI Unsupervised Multivariate LSTM Autoencoder Project Overview This project implements an unsupervised multivariate Long Short-Term Memory (LSTM) ...

Isolation Forest detects anomalies by isolating observations. It builds binary trees (called iTrees) by recursively ...

This repository contains the official PyTorch implementation of our paper: "TransMamba: Time-Frequency Discriminative Feature Learning for Multivariate Time Series Anomaly Detection" (Submitted to ...