Real Time Anomaly Detection

Real-time anomaly detection focuses on swiftly identifying unusual patterns or events within data streams, aiming for immediate responses and proactive interventions. Current research emphasizes efficient algorithms and model architectures, including convolutional autoencoders, recurrent neural networks (like LSTMs and IndRNNs), and large language models, often combined with data sketching or other dimensionality reduction techniques to handle high-dimensional data and resource constraints. This field is crucial for diverse applications, from predictive maintenance in industrial settings and safeguarding critical infrastructure (like power grids) to enhancing the safety and reliability of autonomous systems and improving medical diagnostics.