Non-dominated solutions for time series learning and forecasting. Generating models with a generic two-phase Pareto loca search with VND

DOI10.1007/s11590-021-01720-5OpenAlexW3135786680MaRDI QIDQ2070137

Vitor N. Coelho, Roozbeh Haghnazar Koochaksaraei

Publication date: 21 January 2022

Published in: Optimization Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11590-021-01720-5

zbMATH Keywords

multi-objective optimizationtime seriesforecastingmicrogridsdata visualizationnon-dominated solutionsVNDHFM

Mathematics Subject Classification ID

Statistics (62-XX) Multi-objective and goal programming (90C29)

Uses Software

• Valgrind
• REDD

Cites Work

• Unnamed Item
• Two-phase Pareto local search for the biobjective traveling salesman problem
• Variable neighbourhood search: Methods and applications
• A review of fuzzy set aggregation connectives
• Variable neighborhood search
• A multi-objective green UAV routing problem
• Generic Pareto local search metaheuristic for optimization of targeted offers in a bi-objective direct marketing campaign
• EEG time series learning and classification using a hybrid forecasting model calibrated with GVNS
• A new approach to generate pattern-efficient sets of non-dominated vectors for multi-objective optimization
• Forecasting of Cryptocurrency Prices Using Machine Learning
• Handbook of metaheuristics

This page was built for publication: Non-dominated solutions for time series learning and forecasting. Generating models with a generic two-phase Pareto loca search with VND