dc.contributor |
Fortiana Gregori, Josep |
|
dc.creator |
Casals Lladó, Núria |
|
dc.date |
2018-09-26T08:35:13Z |
|
dc.date |
2018-09-26T08:35:13Z |
|
dc.date |
2018-06-27 |
|
dc.date.accessioned |
2024-12-16T10:26:43Z |
|
dc.date.available |
2024-12-16T10:26:43Z |
|
dc.identifier |
http://hdl.handle.net/2445/124823 |
|
dc.identifier.uri |
http://fima-docencia.ub.edu:8080/xmlui/handle/123456789/21462 |
|
dc.description |
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Josep Fortiana Gregori, |
|
dc.description |
[en] Recurrent neural networks (RNNs) have been widely used for processing sequential data and are capable of learning long-term dependencies. This project proceeds from its inception, studying the behaviour of the simplest Deep Learning structures, to learning issues associated with time series data analysis to finally achieve more complex architectures: Long Short-Term Memory and Gated Recurrent Units. A model with a Gated Recurrent Unit has been implemented to forecast time series data associated with electricity consumption. |
|
dc.format |
65 p. |
|
dc.format |
application/pdf |
|
dc.language |
cat |
|
dc.rights |
cc-by-nc-nd (c) Núria Casals Lladó, 2018 |
|
dc.rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
dc.source |
Treballs Finals de Grau (TFG) - Matemàtiques |
|
dc.subject |
Xarxes neuronals (Informàtica) |
|
dc.subject |
Algorismes computacionals |
|
dc.subject |
Anàlisi de sèries temporals |
|
dc.subject |
Aprenentatge automàtic |
|
dc.subject |
Energia elèctrica |
|
dc.subject |
Treballs de fi de grau |
|
dc.subject |
Neural networks (Computer science) |
|
dc.subject |
Computer algorithms |
|
dc.subject |
Time-series analysis |
|
dc.subject |
Machine learning |
|
dc.subject |
Electric power |
|
dc.subject |
Bachelor's theses |
|
dc.title |
Xarxes neuronals recurrents per a sèries temporals |
|
dc.type |
info:eu-repo/semantics/bachelorThesis |
|