Anomaly Detection in Streaming Data: Investigating anomaly detection algorithms for identifying outliers in streaming data streams in real-time

Abstract

Anomaly detection in streaming data is a critical task in various domains such as finance, healthcare, and cybersecurity. This paper presents an investigation into anomaly detection algorithms for identifying outliers in streaming data streams in real-time. We discuss the challenges associated with anomaly detection in streaming data and review the state-of-the-art algorithms used for this purpose. We also provide a comparative analysis of these algorithms based on their performance metrics and applicability to different types of streaming data. Finally, we discuss future research directions and potential applications of anomaly detection in streaming data.