Generalized relational tensors for chaotic time series
The article deals with a generalized relational tensor, a novel discrete structure to store information about a time series, and algorithms (1) to fill the structure, (2) to generate a time series from the structure, and (3) to predict a time series. The algorithms combine the concept of generalized...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2023-03-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1254.pdf |
| _version_ | 1811158100473806848 |
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| author | Vasilii A. Gromov Yury N. Beschastnov Korney K. Tomashchuk |
| author_facet | Vasilii A. Gromov Yury N. Beschastnov Korney K. Tomashchuk |
| author_sort | Vasilii A. Gromov |
| collection | DOAJ |
| description | The article deals with a generalized relational tensor, a novel discrete structure to store information about a time series, and algorithms (1) to fill the structure, (2) to generate a time series from the structure, and (3) to predict a time series. The algorithms combine the concept of generalized z-vectors with ant colony optimization techniques. To estimate the quality of the storing/re-generating procedure, a difference between the characteristics of the initial and regenerated time series is used. For chaotic time series, a difference between characteristics of the initial time series (the largest Lyapunov exponent, the auto-correlation function) and those of the time series re-generated from a structure is used to assess the effectiveness of the algorithms in question. The approach has shown fairly good results for periodic and benchmark chaotic time series and satisfactory results for real-world chaotic data. |
| first_indexed | 2024-04-10T05:18:39Z |
| format | Article |
| id | doaj.art-e730696d264948c49af93df7c51fcca5 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-04-10T05:18:39Z |
| publishDate | 2023-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-e730696d264948c49af93df7c51fcca52023-03-08T15:05:13ZengPeerJ Inc.PeerJ Computer Science2376-59922023-03-019e125410.7717/peerj-cs.1254Generalized relational tensors for chaotic time seriesVasilii A. Gromov0Yury N. Beschastnov1Korney K. Tomashchuk2School of Data Analysis and Artificial Intelligence, Higher School Economics University, Moscow, RussiaSchool of Data Analysis and Artificial Intelligence, Higher School Economics University, Moscow, RussiaSchool of Data Analysis and Artificial Intelligence, Higher School Economics University, Moscow, RussiaThe article deals with a generalized relational tensor, a novel discrete structure to store information about a time series, and algorithms (1) to fill the structure, (2) to generate a time series from the structure, and (3) to predict a time series. The algorithms combine the concept of generalized z-vectors with ant colony optimization techniques. To estimate the quality of the storing/re-generating procedure, a difference between the characteristics of the initial and regenerated time series is used. For chaotic time series, a difference between characteristics of the initial time series (the largest Lyapunov exponent, the auto-correlation function) and those of the time series re-generated from a structure is used to assess the effectiveness of the algorithms in question. The approach has shown fairly good results for periodic and benchmark chaotic time series and satisfactory results for real-world chaotic data.https://peerj.com/articles/cs-1254.pdfChaotic time seriesGraph representation of a seriesAnt colony optimizationIrregularly sampled time series |
| spellingShingle | Vasilii A. Gromov Yury N. Beschastnov Korney K. Tomashchuk Generalized relational tensors for chaotic time series PeerJ Computer Science Chaotic time series Graph representation of a series Ant colony optimization Irregularly sampled time series |
| title | Generalized relational tensors for chaotic time series |
| title_full | Generalized relational tensors for chaotic time series |
| title_fullStr | Generalized relational tensors for chaotic time series |
| title_full_unstemmed | Generalized relational tensors for chaotic time series |
| title_short | Generalized relational tensors for chaotic time series |
| title_sort | generalized relational tensors for chaotic time series |
| topic | Chaotic time series Graph representation of a series Ant colony optimization Irregularly sampled time series |
| url | https://peerj.com/articles/cs-1254.pdf |
| work_keys_str_mv | AT vasiliiagromov generalizedrelationaltensorsforchaotictimeseries AT yurynbeschastnov generalizedrelationaltensorsforchaotictimeseries AT korneyktomashchuk generalizedrelationaltensorsforchaotictimeseries |