Time series sampling
Resumen:
Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input X, which is a time series, and an output Y = f(X) where f is a very complicated function, whose computational cost for every new input is very high. We are given two sets of observations of X, S1 and S2 of different sizes such that only f(S1) isavailable. We tackle the problem of selecting a subsample S3 ∈ S2 of a smaller size on which to run the complex model f and such that distribution of f(S3) is close to that of f(S1). We adapt to this new framework five algorithms introduced in a previous work "Subsampling under Distributional Constraints" to solve this problem and show their efficiency using time series data.
| 2022 | |
|
Optimal sampling Kolmogorov–Smirnov Time series Encoding Dynamic time warping |
|
| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/41080 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| _version_ | 1807522801704239104 |
|---|---|
| author | Combes, Florian |
| author2 | Fraiman, Ricardo Ghattas, Badih |
| author2_role | author author |
| author_facet | Combes, Florian Fraiman, Ricardo Ghattas, Badih |
| author_role | author |
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| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 MD5 MD5 MD5 |
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| collection | COLIBRI |
| dc.contributor.filiacion.none.fl_str_mv | Combes Florian Fraiman Ricardo, Universidad de la República (Uruguay). Facultad de Ciencias. Centro de Matemática. Ghattas Badih |
| dc.creator.none.fl_str_mv | Combes, Florian Fraiman, Ricardo Ghattas, Badih |
| dc.date.accessioned.none.fl_str_mv | 2023-11-14T12:34:05Z |
| dc.date.available.none.fl_str_mv | 2023-11-14T12:34:05Z |
| dc.date.issued.none.fl_str_mv | 2022 |
| dc.description.abstract.none.fl_txt_mv | Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input X, which is a time series, and an output Y = f(X) where f is a very complicated function, whose computational cost for every new input is very high. We are given two sets of observations of X, S1 and S2 of different sizes such that only f(S1) isavailable. We tackle the problem of selecting a subsample S3 ∈ S2 of a smaller size on which to run the complex model f and such that distribution of f(S3) is close to that of f(S1). We adapt to this new framework five algorithms introduced in a previous work "Subsampling under Distributional Constraints" to solve this problem and show their efficiency using time series data. |
| dc.description.es.fl_txt_mv | Este artículo forma parte de las actas del "The 8th International Conference on Time Series and Forecasting." |
| dc.format.extent.es.fl_str_mv | 7 h. |
| dc.format.mimetype.es.fl_str_mv | application/pdf |
| dc.identifier.citation.es.fl_str_mv | Combes, F, Fraiman, R y Ghattas, B. "Time series sampling". Engineering Proceedings. [en línea] 2022, 18: 32. 7 h. DOI:10.3390/engproc2022018032 |
| dc.identifier.doi.none.fl_str_mv | 10.3390/engproc2022018032 |
| dc.identifier.eissn.none.fl_str_mv | 2673-4591 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12008/41080 |
| dc.language.iso.none.fl_str_mv | en eng |
| dc.publisher.es.fl_str_mv | MDPI |
| dc.relation.ispartof.es.fl_str_mv | Engineering Proceedings, 2022, 18: 32 |
| dc.rights.license.none.fl_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:COLIBRI instname:Universidad de la República instacron:Universidad de la República |
| dc.subject.es.fl_str_mv | Optimal sampling Kolmogorov–Smirnov Time series Encoding Dynamic time warping |
| dc.title.none.fl_str_mv | Time series sampling |
| dc.type.es.fl_str_mv | Artículo |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/article |
| dc.type.version.none.fl_str_mv | info:eu-repo/semantics/publishedVersion |
| description | Este artículo forma parte de las actas del "The 8th International Conference on Time Series and Forecasting." |
| eu_rights_str_mv | openAccess |
| format | article |
| id | COLIBRI_baf0974f20c2f0952f0641c713fb4a40 |
| identifier_str_mv | Combes, F, Fraiman, R y Ghattas, B. "Time series sampling". Engineering Proceedings. [en línea] 2022, 18: 32. 7 h. DOI:10.3390/engproc2022018032 10.3390/engproc2022018032 2673-4591 |
| instacron_str | Universidad de la República |
| institution | Universidad de la República |
| instname_str | Universidad de la República |
| language | eng |
| language_invalid_str_mv | en |
| network_acronym_str | COLIBRI |
| network_name_str | COLIBRI |
| oai_identifier_str | oai:colibri.udelar.edu.uy:20.500.12008/41080 |
| publishDate | 2022 |
| reponame_str | COLIBRI |
| repository.mail.fl_str_mv | mabel.seroubian@seciu.edu.uy |
| repository.name.fl_str_mv | COLIBRI - Universidad de la República |
| repository_id_str | 4771 |
| rights_invalid_str_mv | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| spelling | Combes FlorianFraiman Ricardo, Universidad de la República (Uruguay). Facultad de Ciencias. Centro de Matemática.Ghattas Badih2023-11-14T12:34:05Z2023-11-14T12:34:05Z2022Combes, F, Fraiman, R y Ghattas, B. "Time series sampling". Engineering Proceedings. [en línea] 2022, 18: 32. 7 h. DOI:10.3390/engproc2022018032https://hdl.handle.net/20.500.12008/4108010.3390/engproc20220180322673-4591Este artículo forma parte de las actas del "The 8th International Conference on Time Series and Forecasting."Some complex models are frequently employed to describe physical and mechanical phenomena. In this setting, we have an input X, which is a time series, and an output Y = f(X) where f is a very complicated function, whose computational cost for every new input is very high. We are given two sets of observations of X, S1 and S2 of different sizes such that only f(S1) isavailable. We tackle the problem of selecting a subsample S3 ∈ S2 of a smaller size on which to run the complex model f and such that distribution of f(S3) is close to that of f(S1). We adapt to this new framework five algorithms introduced in a previous work "Subsampling under Distributional Constraints" to solve this problem and show their efficiency using time series data.Submitted by Egaña Florencia (florega@gmail.com) on 2023-11-13T18:36:25Z No. of bitstreams: 2 license_rdf: 25790 bytes, checksum: 489f03e71d39068f329bdec8798bce58 (MD5) engproc-18-00032-v2-1.pdf: 336351 bytes, checksum: 2ba0f089e01b257bb18b048bd65ebc9c (MD5)Approved for entry into archive by Faget Cecilia (lfaget@fcien.edu.uy) on 2023-11-13T18:40:11Z (GMT) No. of bitstreams: 2 license_rdf: 25790 bytes, checksum: 489f03e71d39068f329bdec8798bce58 (MD5) engproc-18-00032-v2-1.pdf: 336351 bytes, checksum: 2ba0f089e01b257bb18b048bd65ebc9c (MD5)Made available in DSpace by Luna Fabiana (fabiana.luna@seciu.edu.uy) on 2023-11-14T12:34:05Z (GMT). No. of bitstreams: 2 license_rdf: 25790 bytes, checksum: 489f03e71d39068f329bdec8798bce58 (MD5) engproc-18-00032-v2-1.pdf: 336351 bytes, checksum: 2ba0f089e01b257bb18b048bd65ebc9c (MD5) Previous issue date: 20227 h.application/pdfenengMDPIEngineering Proceedings, 2022, 18: 32Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014)info:eu-repo/semantics/openAccessLicencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)Optimal samplingKolmogorov–SmirnovTime seriesEncodingDynamic time warpingTime series samplingArtículoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionreponame:COLIBRIinstname:Universidad de la Repúblicainstacron:Universidad de la RepúblicaCombes, FlorianFraiman, RicardoGhattas, BadihLICENSElicense.txtlicense.txttext/plain; charset=utf-84267http://localhost:8080/xmlui/bitstream/20.500.12008/41080/5/license.txt6429389a7df7277b72b7924fdc7d47a9MD55CC-LICENSElicense_urllicense_urltext/plain; charset=utf-850http://localhost:8080/xmlui/bitstream/20.500.12008/41080/2/license_urla006180e3f5b2ad0b88185d14284c0e0MD52license_textlicense_texttext/html; charset=utf-814674http://localhost:8080/xmlui/bitstream/20.500.12008/41080/3/license_text6eed504571858d3e58aeed5ad67e191aMD53license_rdflicense_rdfapplication/rdf+xml; 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- Universidad de la Repúblicafalse |
| spellingShingle | Time series sampling Combes, Florian Optimal sampling Kolmogorov–Smirnov Time series Encoding Dynamic time warping |
| status_str | publishedVersion |
| title | Time series sampling |
| title_full | Time series sampling |
| title_fullStr | Time series sampling |
| title_full_unstemmed | Time series sampling |
| title_short | Time series sampling |
| title_sort | Time series sampling |
| topic | Optimal sampling Kolmogorov–Smirnov Time series Encoding Dynamic time warping |
| url | https://hdl.handle.net/20.500.12008/41080 |
