Pattern Extraction in High-dimensional Time Series

Temporal data mining has attracted greater interest from researchers in the last decade and different approaches have been proposed. In this context, identifying temporal patterns has been a promising strategy in several areas, which include amino acid sequences data, stock market, medical data like ECG and EEG, among others. This project aims to study and develop methods for supporting the classification and prediction tasks of high dimensionality temporal data, applying pattern identification in association with machine learning techniques.

Research Team

Carlos Andres Ferrero, MSc. (Federal Institute of Santa Catarina - IFSC)

Joylan Nunes Maciel, MSc. (Federal University of Latin-American Integration - UNILA)

Willian Zalewski, MSc. (Federal University of Technology - UTFPR)

Students

Lucas Guilherme Hübner - B.Sc. PIBITI/CNPq

This project is supported by the Laboratory of High Performance Computing (LCAD) of the Federal University of Latin-American Integration (UNILA)