Detection of Anomalies in Time Series from Different Contexts

Our next speaker for HIIT Otaniemi seminar series is Antonio Neme from the "Complex Systems" group of the National Autonomous University of Mexico.

All ICS@Aalto researchers are also warmly welcome to attend the seminar!

HIIT seminar Otaniemi, Monday October 25, 13:15
Location: Computer Science building, hall T2

Short Bio:
Antonio Neme received his Ph.D. degree in computer science from the National Autonomous University of Mexico. He works a researcher at the "Complex Systems" group of the National Autonomous University of Mexico since 2005. He is a visiting researcher in the Department of Information and Computer Science of the Aalto University School of Science and Technology. His research interests include data mining, unsupervised learning, agent-based modeling of social phenomena, and biomathematics.

Title:
Detection of Anomalies in Time Series from Different Contexts

Abstract:
Anomaly detection is an important task in several contexts, including weather conditions, environmental dynamics, biological sequences, and even in the arts. A system able to detect anomalies is presented with instances of normal or habitual behavior, generally in terms of time series, and it has to be able to learn relations and features that characterize that single class. Those relations and features allow the system to detect situations previously unseen. In this seminar, a methodology based in information theory and unsupervised learning is presented. Results covering different contexts are presented. First, in the arts, we present the case of authorship identification for several texts, and we also present the analysis over the writings from Iris Murdoch in both, the novels written in the pre-illness period, and the last novel written with mental handicaps. In the bioinformatics context, we present preliminary results in which our working methodology is able to detect external sequences to a given organism, which is related to horizontal gene transfer detection.