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Hybrid algorithms for function
approximation and time series prediction

Organizer: Ignacio Rojas Ruiz


Hybrid algorithms (mainly based on soft computing techniques) are a powerful tool in function approximation and time series prediction. In function approximation, there is usually trade-off between precision and robustness and precision and simplicity. Time series analysis includes three important specific problems: prediction, modeling, and characterization. The goal of prediction is to accurately forecast the short-term evolution of the system, the aim of modelling is to precisely capture the features of the long-term behaviour of the system, and the purpose of system characterization is to determine some underlying fundamental properties of the system. Papers concerning these goals, using traditional statistical model, neural networks, soft-computing techniques, fuzzy system, etc are welcome.

To submit papers click here.

  • Ignacio Rojas Ruiz
  • Departamento de Arquitectura y Tecnología de Computadores
  • Escuela Técnica Superior de Ingeniería Informática
  • Campus Aynadamar, C/ Daniel Saucedo Aranda s/n
  • Universidad de Granada E-18071 GRANADA (Spain)
  • Phone: +34-958- 24 61 28 - Fax: +34-958- 24 89 93

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