Foundation models for time series anomaly detection

This project aims to develop foundation models for detecting anomalies in time series data. Anomalies, such as unusual patterns or unexpected events, can signal critical issues in systems like healthcare, finance, or cybersecurity. Current methods are often limited by scarcity of anomaly labels. By leveraging advanced machine learning techniques, this project seeks to create robust models that can generalize across diverse time series scenarios. The expected outcomes include improved accuracy in detecting anomalies. The benefits span various sectors, including cybersecurity and healthcare.