Combining Singular-Spectrum Analysis and neural networks for time series forecasting

F. Lisi, O. Nicolis, Marco Sandri

Resultado de la investigación: Contribución a una revistaArtículorevisión exhaustiva

23 Citas (Scopus)

Resumen

In this paper, we propose a combination of an adaptive noise-reduction algorithm based on Singular-Spectrum Analysis (SSA) and a standard feedforward neural prediction model. We test the forecast skill of our method on some short real-world and computergenerated time series with different amounts of additive noise. The results show that our combined technique has better performances than those offered by the same network directly applied to raw data, and therefore is well suited to forecast short and noisy time series with an underlying deterministic data generating process (DGP).

Idioma originalInglés
Páginas (desde-hasta)6-10
Número de páginas5
PublicaciónNeural Processing Letters
Volumen2
N.º4
DOI
EstadoPublicada - 1 jul 1995

Áreas temáticas de ASJC Scopus

  • Software
  • Neurociencia (todo)
  • Redes de ordenadores y comunicaciones
  • Inteligencia artificial

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