# name
Xarxes neuronals recurrents per a sèries temporals

# metadata.dc.description[].value
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2018, Director: Josep Fortiana Gregori,

# metadata.dc.description.abstract[].value
[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.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Núria Casals Lladó, 2018

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Matemàtiques

# metadata.dc.title[].value
Xarxes neuronals recurrents per a sèries temporals