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dc.contributor.author | Tuhkuri, Joonas | es_ES |
dc.date.accessioned | 2018-01-29T08:12:03Z | |
dc.date.available | 2018-01-29T08:12:03Z | |
dc.date.issued | 2016-10-10 | |
dc.identifier.isbn | 9788490484623 | |
dc.identifier.uri | http://hdl.handle.net/10251/95626 | |
dc.description | Abstract de la ponencia | es_ES |
dc.description.abstract | [EN] In this paper we document the ETLAnow project. ETLAnow is a model for forecasting with big data. At the moment, it predicts the unemployment rate in the EU-28 countries using Google search data. The model is publicly available at the ETLAnow’s website, http://www.etlanow.eu. The forecast model is based on the idea that volumes of Google searches could be associated with the current and future level of an economic index. And these data are available earlier than official statistics. The motivation for our approach is that big data could help produce more accurate economic forecasts. Those forecasts would inform better policy and decisions, and help real people—especially during an economic crisis. | es_ES |
dc.format.extent | 1 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Editorial Universitat Politècnica de València | es_ES |
dc.relation.ispartof | CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | web data | es_ES |
dc.subject | internet data | es_ES |
dc.subject | big data | es_ES |
dc.subject | qca | es_ES |
dc.subject | pls | es_ES |
dc.subject | sem | es_ES |
dc.subject | conference | es_ES |
dc.title | ETLAnow: A Model for Forecasting with Big Data | es_ES |
dc.type | Capítulo de libro | es_ES |
dc.type | Comunicación en congreso | es_ES |
dc.identifier.doi | 10.4995/CARMA2016.2015.4224 | |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Tuhkuri, J. (2016). ETLAnow: A Model for Forecasting with Big Data. En CARMA 2016: 1st International Conference on Advanced Research Methods in Analytics. Editorial Universitat Politècnica de València. 116-116. https://doi.org/10.4995/CARMA2016.2015.4224 | es_ES |
dc.description.accrualMethod | OCS | es_ES |
dc.relation.conferencename | CARMA 2016 - 1st International Conference on Advanced Research Methods and Analytics | es_ES |
dc.relation.conferencedate | July 06-07,2016 | es_ES |
dc.relation.conferenceplace | Valencia, Spain | es_ES |
dc.relation.publisherversion | http://ocs.editorial.upv.es/index.php/CARMA/CARMA2016/paper/view/4224 | es_ES |
dc.description.upvformatpinicio | 116 | es_ES |
dc.description.upvformatpfin | 116 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.relation.pasarela | OCS\4224 | es_ES |