Universidade Federal do Ceará
Mestrado e Doutorado em Ciências da Computação

Título: Time Series Anomaly Detection and Diagnosis via Manifold Learning and Normalizing Flows

Data: 21/08/2024
Horário: 09h00
Local:  Sala de Seminários – Bloco 952 e Google Meet (https://meet.google.com/bke-kdsn-afn)

Resumo:

The emergence of data collection and storage technologies has allowed the accumulation of extensive data over time. As a consequence, high-dimensional time series data sets have become prevalent across diverse fields, including sensor networks, security, healthcare, manufacturing, and finances. In this context, although it represents a substantial challenge, the detection of rare events in such data sets is a fundamental task in several applications, including, cyber-intrusion detection, defect analysis, fault detection, credit card fraud analysis, suspicious trajectory detection, etc. In order to handle this challenge, various methods have been employed, ranging from traditional approaches such as classification, clustering, distance metrics, density estimation and statistical techniques to more contemporary solutions involving deep learning models. In this thesis we provide a comprehensive overview of the field, as well present the state-of-the-art in multivariate time series anomaly detection and diagnosis, and also introduce two new approaches for solving problems involving anomaly detection in time series. The first one, called aggregated anomaly detection with normalizing flows (GRADINGS) is a framework for anomaly detection in time series database, applied to trajectory data that is based on estimating the density for each trajectories segments and aggregating segments’ likelihoods into a single anomaly score. Such a strategy enables handling possibly large sequences with different lengths. The second approach, called robust anomaly detection on multivariate time series (RANDOMS), which uses normalization flows and manifold learning techniques to solve the anomaly detection and diagnosis problems. Extensive evaluations of the proposed techniques have been conducted across a wide range of applications, comparing it against several models. The results of our computational experiments demonstrate the efficacy of our approaches, consistently outperforming existing state-of-the-art anomaly detection and diagnosis methods in numerous cases.

Banca examinadora:

• Prof. Dr. César Lincoln Cavalcante Mattos (MDCC/UFC – Orientador)
• Prof. Dr. José Antonio Fernandes de Macêdo (MDCC/UFC)
• Prof. Dr. Anselmo Ramalho Pitombeira Neto (UFC)
• Prof. Dr. Bruno José Torres Fernandes (UPE)
• Prof. Dr. Rogério Galante Negri (UNESP)