Dissertation (January 12, 2026): Edson Paulo da Silva Pinto Sobrinho
Student: Edson Paulo da Silva Pinto Sobrinho
Title: Fine-tuning detection criteria to enhance anomaly identification in time series
Advisors: Eduardo Soares Ogasawara (advisor) and Kele Teixeira Belloze (co-advisor)
Committee: Eduardo Soares Ogasawara (CEFET/RJ), Kele Teixeira Belloze (CEFET/RJ), Rafaelli de Carvalho Coutinho (CEFET/RJ) and Fábio André Machado Porto (LNCC)
Day/Hour: January 12, 2026 / 2 p.m.
Room: https://teams.microsoft.com/
Abstract: Anomaly Detection (AD) is the problem of identifying observations that do not conform to typical ones in a time series. Detection methods implicitly define detection criteria, such as deviation measures, filter thresholds, and candidate anomaly selection strategies. Choosing inappropriate criteria results in inaccurate outputs, generating spurious alerts or missing events. Adjusting these criteria is essential for monitoring systems. To address this challenge, this study explores the fine-tuning of deviation measures, filter thresholds, and candidate selection strategies. Experimental results show that the proper choice of criteria significantly improves AD performance, often with greater impact than changing the detection methods.