A Survey of Advancements in Anomaly Detection for Multivariate Time Series Data
Ambati Saritha, Macherla Dhana Lakshmi · 2024
Identification of anomalies in multivariate time series data is a critical challenge in various domains, including IT systems, IoT, industrial applications, and traffic monitoring, etc. This abstract provides a review of recent research papers that innovative approaches to address this challenge. These methods aim to enhance the accuracy, efficiency, and generalization ability of algorithms that detect anomalies. this research paper represents a diverse range of pioneering approaches to notice anomalies in multivariate time series data. They address the need for accuracy, efficiency, and robustness in detecting anomalies across various domains, from IT systems to IoT and industrial applications. These advancements contribute to the growing field of anomaly detection, offering valuable solutions for real-world challenges.