Observatorio de I+D+i UPM

Memorias de investigación
Communications at congresses:
Time Series Wavelet Mutiresolution Prediction Under Hurst Exponent Study, Neural Classifiers Application
Year:2009
Research Areas
  • Processing and signal analysis
Information
Abstract
In this paper it is presented a study about the convenience of applying a wavelet multiresolution analysis to analyze and forecast a time series based on the Hurst exponent calculation. It is also presented the direct application to complex neural networks classification stages design. The Hurst exponent analysis gives an approximation of the predictability of a time series, so this point gives the key information to understand if a time series can be analyzed in a classical analysis-synthesis wavelet analysis and the optimum decomposition degree level. This criterion can be directly translated in the feature selection stage in a Neural Classifier design. A rainfall time series is studied as a case study performing two different wavelet analysis and selecting the best one in terms of the Hurst¿s Exponent
International
Si
Congress
1st European Workshop on Turbulence and Fractals
960
Place
Madrid, España
Reviewers
Si
ISBN/ISSN
1870-4069
Start Date
10/12/2009
End Date
10/12/2009
From page
1
To page
5
Proc. of 1st European Workshop on Turbulence and Fractals
Participants
  • Autor: Gregorio Buendía (Inst. Meteorología)
  • Autor: Diego Andina De la Fuente (UPM)
  • Autor: Fulgencio S Buendía (GMV)
  • Autor: Ana Maria Tarquis Alfonso (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Automatización en Señal y Comunicaciones (GASC)
  • Centro o Instituto I+D+i: Centro de Estudios e Investigación para la Gestión de Riesgos Agrarios Medioambientales (CEIGRAM)
  • Departamento: Matemática Aplicada a la Ingeniería Agronómica
  • Departamento: Señales, Sistemas y Radiocomunicaciones
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