Publication: On projection methods for functional time series forecasting
dc.affiliation.dpto | UC3M. Departamento de Estadística | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Técnicas no Paramétricas y de Computación Intensiva en Estadística | es |
dc.contributor.author | Elías Fernández, Antonio | |
dc.contributor.author | Jiménez Recaredo, Raúl José | |
dc.contributor.author | Shang, Han Lin | |
dc.date.accessioned | 2023-06-20T09:58:56Z | |
dc.date.available | 2023-06-20T09:58:56Z | |
dc.date.issued | 2021-11-01 | |
dc.description.abstract | Two nonparametric methods are presented for forecasting functional time series (FTS). The FTS we observe is a curve at a discrete-time point. We address both one-step-ahead forecasting and dynamic updating. Dynamic updating is a forward prediction of the unobserved segment of the most recent curve. Among the two proposed methods, the first one is a straightforward adaptation to FTS of the k-nearest neighbors methods for univariate time series forecasting. The second one is based on a selection of curves, termed the curve envelope, that aims to be representative in shape and magnitude of the most recent functional observation, either a whole curve or the observed part of a partially observed curve. In a similar fashion to k-nearest neighbors and other projection methods successfully used for time series forecasting, we "project" the k-nearest neighbors and the curves in the envelope for forecasting. In doing so, we keep track of the next period evolution of the curves. The methods are applied to simulated data, daily electricity demand, and NOx emissions and provide competitive results with and often superior to several benchmark predictions. The approach offers a model-free alternative to statistical methods based on FTS modeling to study the cyclic or seasonal behavior of many FTS. | en |
dc.description.sponsorship | The authors acknowledge insightful comments and suggestions from two reviewers and the Associate Editor. Antonio Elías is supported by the Spanish Ministerio de Educación, Cultura y Deporte under grant FPU15/00625 and the research stay grant EST17/00841. Antonio Elías and Raúl Jiménez are partially supported by the Spanish Ministerio de Economía y Competitividad under grant ECO2015-66593-P and PID2019-109196GB-I00/AEI/10.13039/501100011033. Funding for open access charge: Universidad de Málaga / CBUA. Part of this article was conducted during a stay at Australian National University. Antonio Elías is grateful to Han Lin Shang for his hospitality and insightful and constructive discussions. | en |
dc.format.extent | 13 | es |
dc.identifier.bibliographicCitation | Elías, A., Jiménez, R., & Shang, H. L. (2021). On projection methods for functional time series forecasting. Journal of Multivariate Analysis, 189, 104890. | en |
dc.identifier.doi | https://doi.org/10.1016/j.jmva.2021.104890 | |
dc.identifier.issn | 0047-259X | |
dc.identifier.publicationfirstpage | 1 | es |
dc.identifier.publicationissue | 104890 | es |
dc.identifier.publicationlastpage | 13 | es |
dc.identifier.publicationtitle | Journal of Multivariate Analysis | en |
dc.identifier.publicationvolume | 189 | es |
dc.identifier.uri | https://hdl.handle.net/10016/37537 | |
dc.identifier.uxxi | AR/0000028861 | |
dc.language.iso | eng | en |
dc.publisher | Elsevier | en |
dc.relation.projectID | Gobierno de España. ECO2015-66593-P | es |
dc.relation.projectID | Gobierno de España. PID2019-109196GB-I00 | es |
dc.relation.projectID | Gobierno de España. FPU15/00625 | es |
dc.relation.projectID | Gobierno de España. EST17/00841 | es |
dc.rights | © 2021 The Author(s). | en |
dc.rights | Atribución 3.0 España | * |
dc.rights.accessRights | open access | en |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject.eciencia | Estadística | es |
dc.subject.other | Forecasting | en |
dc.subject.other | Functional depth | en |
dc.subject.other | Functional nonparametric | en |
dc.subject.other | Functional time series | en |
dc.subject.other | Nearest neighbors | en |
dc.title | On projection methods for functional time series forecasting | en |
dc.type | research article | * |
dc.type.hasVersion | VoR | * |
dspace.entity.type | Publication |
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