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dc.contributor.authorStoean, Ruxandra
dc.date.accessioned2019-04-18T09:12:19Z
dc.date.available2019-04-18T09:12:19Z
dc.date.created2019
dc.date.issued2019-04-18
dc.identifier.urihttps://hdl.handle.net/10630/17529
dc.description.abstractStock price prediction is one very challenging and desirable real-world task. The challenge comes from the very dynamic nature of stock movement that is triggered by many different known and unknown factors. An accurate prediction is naturally connected to money gain. In this tutorial, two deep learning architectures will be employed to model such time series data, namely the long short-term memory networks and the temporal convolutional neural networks. The implementation will be performed in Python, using Keras API with Tensorflow backend.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAprendizaje automático (Inteligencia artificial)en_US
dc.subject.otherLSTMen_US
dc.subject.otherDeep Learningen_US
dc.subject.otherAprendizaje profundoen_US
dc.titleOn building LSTM and CNN architectures for modeling time series dataen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroEscuela de Ingenierías Industrialesen_US
dc.relation.eventdateJunio 2019en_US


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