Real-time detection of anomalies in data streams is a foundation of modern applied analysis in complex systems. It enables experts to design rapid, efficient, reliable, and high-performance decision ...

Artificial intelligence (AI) is rapidly becoming one of the most significant drivers of global electricity demand. By 2030, data centre consumption is expected to more than double, from 415 TWh to 945 ...

This demo highlights how one can use a semi-supervised machine learning technique based on autoencoder to detect an anomaly in sensor data (output pressure of a triplex pump). The demo also shows how ...

Cloud infrastructure anomalies cause significant downtime and financial losses (estimated at $2.5 M/hour for major services). Traditional anomaly detection methods fail to capture complex dependencies ...

This repository contains scripts to analyze publicly available log data sets (HDFS, BGL, OpenStack, Hadoop, Thunderbird, ADFA, AWSCTD) that are commonly used to evaluate sequence-based anomaly ...

Abstract: Anomaly detection in time series data is a crucial task with applications spanning various industries. Uncovering unusual patterns within temporal datasets can lead to insights and early ...

As a crucial technique for identifying irregular samples or outlier patterns, anomaly detection has broad applications in many fields. Convex analysis (CA) is one of the fundamental methods used in ...