SAILS: Spectral analysis in linear systems
Autoregressive modelling provides a powerful and flexible parametric approach to modelling uni- or multi-variate time-series data. AR models have mathematical links to linear timeinvariant systems, digital filters and Fourier based frequency analyses. As such, a wide range of time-domain and frequen...
| Main Authors: | , |
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| Format: | Journal article |
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Open Journals
2020
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| _version_ | 1826261934533509120 |
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| author | Quinn, A Hymers, M |
| author_facet | Quinn, A Hymers, M |
| author_sort | Quinn, A |
| collection | OXFORD |
| description | Autoregressive modelling provides a powerful and flexible parametric approach to modelling
uni- or multi-variate time-series data. AR models have mathematical links to linear timeinvariant systems, digital filters and Fourier based frequency analyses. As such, a wide range
of time-domain and frequency-domain metrics can be readily derived from the fitted autoregressive parameters. These approaches are fundamental in a wide range of science and
engineering fields and still undergoing active development. SAILS (Spectral Analysis in Linear
Systems) is a python package which implements such methods and provides a basis for both
the straightforward fitting of AR models as well as exploration and development of newer
methods, such as the decomposition of autoregressive parameters into eigenmodes.
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| first_indexed | 2024-03-06T19:28:23Z |
| format | Journal article |
| id | oxford-uuid:1c95fe30-ce3e-4f4d-bbc9-7351062b004e |
| institution | University of Oxford |
| last_indexed | 2024-03-06T19:28:23Z |
| publishDate | 2020 |
| publisher | Open Journals |
| record_format | dspace |
| spelling | oxford-uuid:1c95fe30-ce3e-4f4d-bbc9-7351062b004e2022-03-26T11:06:24ZSAILS: Spectral analysis in linear systemsJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:1c95fe30-ce3e-4f4d-bbc9-7351062b004eSymplectic ElementsOpen Journals2020Quinn, AHymers, MAutoregressive modelling provides a powerful and flexible parametric approach to modelling uni- or multi-variate time-series data. AR models have mathematical links to linear timeinvariant systems, digital filters and Fourier based frequency analyses. As such, a wide range of time-domain and frequency-domain metrics can be readily derived from the fitted autoregressive parameters. These approaches are fundamental in a wide range of science and engineering fields and still undergoing active development. SAILS (Spectral Analysis in Linear Systems) is a python package which implements such methods and provides a basis for both the straightforward fitting of AR models as well as exploration and development of newer methods, such as the decomposition of autoregressive parameters into eigenmodes. |
| spellingShingle | Quinn, A Hymers, M SAILS: Spectral analysis in linear systems |
| title | SAILS: Spectral analysis in linear systems |
| title_full | SAILS: Spectral analysis in linear systems |
| title_fullStr | SAILS: Spectral analysis in linear systems |
| title_full_unstemmed | SAILS: Spectral analysis in linear systems |
| title_short | SAILS: Spectral analysis in linear systems |
| title_sort | sails spectral analysis in linear systems |
| work_keys_str_mv | AT quinna sailsspectralanalysisinlinearsystems AT hymersm sailsspectralanalysisinlinearsystems |