This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn ...

Acceldata, a leading provider of data observability and agentic data management solutions, is announcing a new capability designed to amplify the power of agentic reasoning within the company’s xLake ...

Anomaly detection in complex crowd scenes is a challenging task due to the inherent variability in crowd behaviors, interactions, and scales. This paper proposes a novel hybrid model that ...

This project demonstrates how an LSTM Autoencoder can be effectively used for anomaly detection in financial time series data. The model successfully identifies potential anomalies in Apple stock ...

The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep Convolutional AutoEncoder (CAE) and deep support vector data description (SVDD) have ...

Anomaly detection for indoor air quality (IAQ) data has become an important area of research as the quality of air is closely related to human health and well-being. However, traditional statistics ...