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Adaptation of classification algorithms to the time series extrinsic regression task

Grant number: 22/00305-5
Support type:Scholarships in Brazil - Scientific Initiation
Effective date (Start): May 01, 2022
Effective date (End): April 30, 2023
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Theory of Computation
Principal researcher:Diego Furtado Silva
Grantee:Guilherme Gomes Arcencio
Home Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

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)

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