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Advances in forecasting model selection based on meta-learning

Grant number: 21/13281-4
Support type:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): May 01, 2022
Effective date (End): April 30, 2023
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal researcher:André Carlos Ponce de Leon Ferreira de Carvalho
Grantee:Moisés Rocha dos Santos
Supervisor abroad: Carlos Manuel Milheiro de Oliveira Pinto Soares
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: Universidade do Porto (UP), Portugal  
Associated to the scholarship:19/10012-2 - Meta-learning for time-series forecasting, BP.DR

Abstract

Forecasting is one of the main tasks of time series analysis. Selecting forecasting models is a crucial decision-making step in many real-world applications. To obtain forecasts that best explain the variables of interest in the future, it is necessary to select the model that best fits a given time series. However, the selection task can be quite computationally expensive due to many available techniques and the possible limitation of highly specialized knowledge. This proposal aims at a hybrid forecasting approach, using meta-learning to select models for components of a time series derived from a decomposition. Meta-learning comes with the proposition of using machine learning techniques to extract knowledge from past tasks and make past tasks faster, more efficient, and better performance. Allied to this, methods with hybrid forecast have shown promising results in the literature. As a result, it is expected to obtain new hybrid models with good performance and a better understanding of the meta-learning process. (AU)

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