Mining Temporal Data using Attribute Extraction

Predictive and descriptive models have been used in different areas supported by machine learning methods. However, the application of traditional ML methods in temporal database has some restrictions such as does not consider the temporal information in the data. Thus, this project aims to construct a structured representation about the temporal data by identifying motifs and calculating statistical and complexity measures.

Research Team

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

Willian Zalewski (Federal University of Technology - UTFPR)

Students:

Gustavo Vieira - B. Sc. PIBIC/Unioeste

Wesley Shann - B. Sc./Unioeste

Sthefano Bandeira - B. Sc./Unioeste

Schematic representation of the Project.