| Grant number: | 23/11745-9 |
| Support Opportunities: | Scholarships abroad - Research Internship - Scientific Initiation |
| Start date: | December 01, 2023 |
| End date: | February 29, 2024 |
| Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques |
| Principal Investigator: | Diego Furtado Silva |
| Grantee: | Andre Guarnier De Mitri |
| Supervisor: | Germain Forestier |
| Host Institution: | Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil |
| Institution abroad: | Université de Haute-Alsace, France |
| Associated to the scholarship: | 23/05041-9 - Adapting Time Series Classification Algorithms to Regression, BP.IC |
Abstract With the growing ubiquity of smartphones, smartwatches, and other devices capable ofcollecting data across time, applications that use time series as input, such as cardiacmonitoring and activity recognition, have become increasingly popular. Several of thesescenarios can be mapped naturally as tasks involving multivariate time series. Thus, various techniques for Machine Learning applied to time series have been developed. However,most of the developed techniques for multivariate time series were adapted from univariatedata. However, there is no clear guidelines to choose the best adaptation for deep learningmultivariate time series classification and extrinsic regression due to the lack of standardizeexperimental evaluation. Considering this scenario, this research will propose, implement,and execute an experimental evaluation procedure to assess different techniques to adaptdeep neural networks designed for univariate time series classification and regression todeal with multivariate problems. | |
| News published in Agência FAPESP Newsletter about the scholarship: | |
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