Overview

Monitoring and analyzing multi-variate data from complex systems is a challenge present across various scientific and industrial fields. Traditional methods have problems with real-time anomaly detection and don’t have the ability to show the causal relationships between variables. Our anomaly detection system (AnomalAIzer) addresses these challenges by combining advanced machine learning algorithms with real-time data processing and causal inference techniques. This solution enables researchers and industries to track, analyze, and respond to complex multi-variate data streams.

Goals

Developing a solution that is able to track root causes of changes that appear in complex multi-variate datasets all while working with near real-time restrictions.

Solution

AnomalAIzer’s core functionality is its advanced causality engine, which maps and analyzes relationships between variables in multi-variate data streams. The system processes incoming data sequentially, adjusting the causal model as new information arrives. There is also a user interface that provides intuitive visualizations of these causal relationships, allowing the user to observe how changes in one variable propagate through the system in real-time.

Results

• Accurate tracking of anomaly root causes, tested on multiple public datasets and achiving high rates of causal impact discoveries.