Prediction of chaotic time series with wavelet coefficients

Naoki Masuda*, Kazuyuki Aihara

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Using the wavelet transform, we can express a time series as a summation of frequency components each of which is localized in the frequency domain. In the present paper, we show that each frequency component given as the wavelet coefficients of a deterministic time series preserves the topological structure of the original dynamical system. We subsequently propose new methods to predict a time series by applying the inverse wavelet transform to predictees of frequency components. Our methods can realize good long-term predictions of deterministic time series contaminated with either high-frequency deterministic noise or white noise.

Original languageEnglish
Pages (from-to)50-59
Number of pages10
JournalElectronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi)
Issue number6
Publication statusPublished - 2001
Externally publishedYes


  • Additive noise
  • Chaos
  • Dyadic wavelet transform
  • Time series predictions
  • Wavelet transform

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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