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

F. Lisi, O. Nicolis, Marco Sandri

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


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).

Original languageEnglish
Pages (from-to)6-10
Number of pages5
JournalNeural Processing Letters
Issue number4
Publication statusPublished - 1 Jul 1995

ASJC Scopus subject areas

  • Software
  • General Neuroscience
  • Computer Networks and Communications
  • Artificial Intelligence


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