title: The method of successive approximations for constructing a model of dynamic polynomial regression
metadata.name: dc.contributor.author
metadata.value: Golovkina, Anna G.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Kozynchenko, Vladimir A.
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Klimenko, Ilia S.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2023-03-14T13:29:35Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2023-03-14T13:29:35Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2022-12
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Golovkina, A. G., Kozynchenko, V. A., & Klimenko, I. S. (2023). The method of successive approximations for constructing a model of dynamic polynomial regression. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 18(4), 487-500.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu10.2022.404
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/39307
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: Predicting the behavior of a certain process in time is an important task that arises in many applied areas, and information about the system that generated this process can either be completely absent or be partially limited. The only available knowledge is the accumulated data on past states and process parameters. Such a task can be successfully solved using machine learning methods, but when it comes to modeling physical experiments or areas where the ability of a model to generalize and interpretability of predictions are important, then the most machine learning methods do not fully satisfy these requirements. The forecasting problem is solved by building a dynamic polynomial regression model, and a method for finding its coefficients is proposed, based on the connection with dynamic systems. Thus, the constructed model corresponds to a deterministic process, potentially described by differential equations, and the relationship between its parameters can be expressed in an analytical form. As an illustration of the applicability of the proposed approach to solving forecasting problems, we consider a synthetic data set generated as a numerical solution of a system of differential equations that describes the Van der Pol oscillator.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: This work was financially supported by St Petersburg State University (project ID 93024916).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: ru
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 18; Issue 4
metadata.lang: -
metadata.name: dc.subject
metadata.value: polynomial regression
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: dynamic systems
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: Taylor map
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: The method of successive approximations for constructing a model of dynamic polynomial regression
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 15509-Текст статьи-51887-1-10-20230305.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/39307/1/15509-%d0%a2%d0%b5%d0%ba%d1%81%d1%82%20%d1%81%d1%82%d0%b0%d1%82%d1%8c%d0%b8-51887-1-10-20230305.pdf
resources.format: Adobe PDF
resources.size: 483,42 kB