2026-05-30T07:06:13Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/175292026-02-03T12:33:57Zcom_10630_2254col_10630_37959

On building LSTM and CNN architectures for modeling time series data Stoean, Ruxandra Aprendizaje automático (Inteligencia artificial) Stock 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. 2019-04-18T09:12:19Z 2019-04-18T09:12:19Z 2019 2019-04-18 conference output https://hdl.handle.net/10630/17529 eng Junio 2019 open access