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Meta-learning for time-series forecasting

Grant number: 19/10012-2
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): June 01, 2019
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal researcher:André Carlos Ponce de Leon Ferreira de Carvalho
Grantee:Moisés Rocha dos Santos
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated research grant:13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry, AP.CEPID
Associated scholarship(s):21/13281-4 - Advances in forecasting model selection based on meta-learning, BE.EP.DR


Time series forecasting has been applied in several real word problems to support the decision-making process, for example Electroencephalogram (EEG) analysis, energy consumption, financial stock market and others. However these experiments usually demand long time to select the best model for decision making. Also for this domain need specific knowledge of the time-serie kind for making accurate forecast. The meta-learning objective is select promising algorithms for new datasets based on meta-dataset knowledge discovery. This project aims to develop a meta-learning based recommendation system of statistical and machine learning models for time-series forecasting. Allied to previous will make an experimental analysis of existing meta-features in time-series and regression domain. (AU)

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