Meta-learning for time-series forecasting

• Grant number: 19/10012-2
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: June 01, 2019
• End date: May 31, 2023
• Field of knowledge: Physical Sciences and Mathematics - Computer Science
• Principal Investigator: André Carlos Ponce de Leon Ferreira de Carvalho
• Grantee: Moisés Rocha dos Santos
• Host 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

Abstract

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)

Scientific publications

(5)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

FERREIRA DE SOUZA, EDUARDO DORNELES; DOS SANTOS, MOISES ROCHA; COSTA DA SILVA, LUCAS CLEOPAS; MUNIZ DE OLIVEIRA, ALEXANDRE CESAR; NETO, AREOLINO DE ALMEIDA; DE ALMEIDA RIBEIRO, PAULO ROGERIO; COTA, VR; BARONE, DAC; DIAS, DRC; DAMAZIO, LCM. Motor Learning and Machine Learning: Predicting the Amount of Sessions to Learn the Tracing Task. COMPUTATIONAL NEUROSCIENCE, v. 1068, p. 10-pg., 2019-01-01. (19/10012-2)

SANTOS, MOISES R.; MUNDIM, LEANDRO R.; CARVALHO, ANDRE C. P. L. F.; DELACAL, EA; FLECHA, JRV; QUINTIAN, H; CORCHADO, E. Evaluation of Error Metrics for Meta-learning Label Definition in the Forecasting Task. HYBRID ARTIFICIAL INTELLIGENT SYSTEMS, HAIS 2020, v. 12344, p. 13-pg., 2020-01-01. (19/10012-2)

SILVESTRE, GABRIEL DALFORNO; DOS SANTOS, MOISES ROCHA; DE CARVALHO, ANDRE C. P. L. F.; IEEE. Seasonal-Trend decomposition based on Loess plus Machine Learning: Hybrid Forecasting for Monthly Univariate Time Series. 2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), v. N/A, p. 7-pg., 2021-01-01. (13/07375-0, 19/10012-2)

SANTOS, MOISES R.; BRAZ, DOUGLAS D. C.; CARVALHO, ANDRE C. P. L. F.; TINOS, RENATO; PAULA, MARCOS B. S.; DORETTO, GABRIEL; GUARNIER, EWERTON; FILHO, DONATO SILVA; SUIAMA, DANILO Y.; FERREIRA, LORENA E.; et al. Machine Learning Approach for Trend Prediction to Improve Returns on Brazilian Energy Market. 2022 IEEE LATIN AMERICAN CONFERENCE ON COMPUTATIONAL INTELLIGENCE (LA-CCI), v. N/A, p. 6-pg., 2022-01-01. (13/07375-0, 19/10012-2)

CASTILHO, DOUGLAS; SANTOS, MOISES R.; TINOS, RENATO; CARVALHO, ANDRE C. P. L. F.; PAULA, MARCOS B. S.; LADEIRA, LUCAS; GUARNIER, EWERTON; SILVA FILHO, DONATO; SUIAMA, DANILO Y.; JUNIOR, EDMUR A. M.; et al. Feature Selection using Complex Networks to Support Price Trend Forecast in Energy Markets. 2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, v. N/A, p. 9-pg., 2023-01-01. (13/07375-0, 19/10012-2)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

SANTOS, Moisés Rocha dos. Algorithm selection and performance understanding for time series forecasting. 2023. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.