000134455 001__ 134455
000134455 005__ 20240424142048.0
000134455 037__ $$aTAZ-TFG-2023-3703
000134455 041__ $$aeng
000134455 1001_ $$aFredes Cáceres, Arturo
000134455 24200 $$aRecurrent Neural Networks for time series forecasting in complex dynamical systems and their applications
000134455 24500 $$aRedes neuronales recurrentes para la predicción de series temporales en sistemas dinámicos complejos y sus aplicaciones
000134455 260__ $$aZaragoza$$bUniversidad de Zaragoza$$c2023
000134455 506__ $$aby-nc-sa$$bCreative Commons$$c3.0$$uhttp://creativecommons.org/licenses/by-nc-sa/3.0/
000134455 520__ $$aArtificial intelligence and machine learning techniques are currently a very hot topic, and recent<br />advances in the field have raised hopes, doubts and fear in society. These techniques are useful<br />for tackling several types of problems like classification, regression, generation of content or<br />forecasting. The focus of this work was set on forecasting time series, which plays an important<br />role in different areas such as business, weather or economics providing insights into future trends<br />or outcomes.<br />The objective was to a study how recurrent neural networks perform when forecasting future<br />steps of time series, by working with different sets of data of increasing complexity. A proof of<br />concept was made working with one-dimensional data of the sum of two waves and some noise.<br />Different types of neurons and architectures were used to compare performance in different<br />scenarios. Next, the study was continued using data from a 3D chaotic system (the Lorenz<br />Attractor), and finally it was attempted to apply these techniques to real business data.<br /><br />
000134455 521__ $$aGraduado en Matemáticas
000134455 540__ $$aDerechos regulados por licencia Creative Commons
000134455 700__ $$aGutiérrez Rodrigo, Sergio$$edir.
000134455 700__ $$aArbiol Herrera, César$$edir.
000134455 7102_ $$aUniversidad de Zaragoza$$bFísica Aplicada$$cFísica Aplicada
000134455 7202_ $$aElipe Sánchez, Antonio$$eponente
000134455 8560_ $$f775551@unizar.es
000134455 8564_ $$s2677404$$uhttps://zaguan.unizar.es/record/134455/files/TAZ-TFG-2023-3703.pdf$$yMemoria (eng)
000134455 909CO $$ooai:zaguan.unizar.es:134455$$pdriver$$ptrabajos-fin-grado
000134455 950__ $$a
000134455 951__ $$adeposita:2024-04-24
000134455 980__ $$aTAZ$$bTFG$$cCIEN
000134455 999__ $$a20230904092647.CREATION_DATE