Adaptation of classification algorithms to the time series extrinsic regression task

• Grant number: 22/00305-5
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Effective date (Start): May 01, 2022
• Effective date (End): August 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Theory of Computation
• Principal Investigator: Diego Furtado Silva
• Grantee: Guilherme Gomes Arcencio
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil
• Associated scholarship(s): 22/12486-4 - From tabular data to time series: novel algorithms for time series extrinsic regression, BE.EP.IC

Abstract

With the growing ubiquity of smartphones, smartwatches, and other devices capable of collecting data across time, applications that use time series as input, such as cardiac monitoring and activity recognition, have become increasingly popular. Thus, various techniques have been developed for Machine Learning applied to time series. However, the majority of those techniques were created for classification and forecasting tasks, which makes the extrinsic regression task lack proper algorithms. Considering that shortage, this project aims to study and adapt the mechanisms used by time series classification algorithms to the extrinsic regression task. The adapted algorithms will be analyzed and compared to traditional regression algorithms and then made available on the time framework. The project also envisages writing a paper compiling the achieved results, as well as publishing all written source code.(AU)