Multi-objective closed-form algebraic expressions discovery approach application to the synthetic time-series generation

Created by W.Langdon from gp-bibliography.bib Revision:1.8612

@Article{Merezhnikov:2021:PCS,

• author = "Mark Merezhnikov and Alexander Hvatov",
• title = "Multi-objective closed-form algebraic expressions discovery approach application to the synthetic time-series generation",
• journal = "Procedia Computer Science",
• year = "2021",
• volume = "193",
• pages = "285--294",
• note = "10th International Young Scientists Conference in Computational Science, YSC2021, 28 June -- 2 July, 2021",
• keywords = "genetic algorithms, genetic programming, model interpretation, evolutionary algorithm, synthetic data, algebraic equation discovery",
• ISSN = "1877-0509",
• URL = "https://www.human-competitive.org/sites/default/files/entry_1.txt",
• URL = "https://www.human-competitive.org/sites/default/files/multi-objective_closed-form_algebraic_expressions_discovery_approach_application_to_the_synthetic_time-series_generation.pdf",
• URL = "https://www.sciencedirect.com/science/article/pii/S1877050921020706",
• DOI = "10.1016/j.procs.2021.10.029",
• abstract = "Time-series modeling is a well-studied topic of classical analysis and machine learning. However, large datasets are required to obtain the model with a better prediction quality with the increasing model complexity. Therefore, some applications demand synthetic datasets that are preserving modeling-sensitive properties. Another application of synthetic data is data anonymization. The synthetic data generation algorithm may be split into two parts: the time-series modeling and the synthetic data generation parts. The model must be interpretable to obtain the synthetic data with good quality. The model parameter interpretation allows controlling generation by adding noise to different groups of parameters. In the paper, the evolutionary multi-objective closed-form algebraic expressions discovery approach that allows obtaining the model in the form that may be analyzed using the mathematics is proposed. The analysis allows the interpretation of the model parameters for the controllable generation of the synthetic data. The notion of synthetic data quality is discussed. The examples of the synthetic time-series generation based on two datasets with different properties are shown.",
• notes = "Entered 2022 HUMIES

  YSC2021 special issue edited by Alexandra Klimova, Angelos Bilas, Vangelis Harmandaris, Evangelia Kalligiannaki, Eleni Kanellou, Alexander Boukhanovsky

  Also known as \cite{MEREZHNIKOV2021285}",

}

Genetic Programming entries for Mark Merezhnikov Alexander A Hvatov

Citations