Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/202946
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dc.contributor.advisorCorcuera Valverde, José Manuel-
dc.contributor.authorCots Mañas, David-
dc.date.accessioned2023-10-19T07:47:58Z-
dc.date.available2023-10-19T07:47:58Z-
dc.date.issued2023-06-13-
dc.identifier.urihttp://hdl.handle.net/2445/202946-
dc.descriptionTreballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2023, Director: José Manuel Corcuera Valverdeca
dc.description.abstract[en] The signature captures a non-parametric characteristic of a data stream that has been previously transformed into a path through an embedding algorithm. In this work, we will introduce the signature method, which consists of embedding a data stream into a path to hen use its signature as a feature in machine learning problems. First, we will define the signature of a path and present its most important properties. Then, we will explore different ways of embedding data streams into a continuous path and examine the application of this method to financial time series data. Finally, we will see how we can implement a supervised machine learning model based on signatures.ca
dc.format.extent43 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isocatca
dc.rightscc-by-nc-nd (c) David Cots Mañas, 2023-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Matemàtiques-
dc.subject.classificationAprenentatge automàticca
dc.subject.classificationTreballs de fi de grau-
dc.subject.classificationMatemàtica financeraca
dc.subject.classificationAnàlisi numèricaca
dc.subject.otherMachine learningen
dc.subject.otherBachelor's theses-
dc.subject.otherBusiness mathematicsen
dc.subject.otherNumerical analysisen
dc.titleEl mètode de la signatura i aplicacions a dades de tipus financerca
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Treballs Finals de Grau (TFG) - Matemàtiques

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