Independent component analysis and resolution pursuit with wavelet and cosine packets

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

We present an application of Independent Component Analysis combined with Multiresolution Analysis and the Matching Pursuit algorithm for exploring high frequency financial time series. We show that these are powerful instruments in the finance data mining domain, especially when dealing with signal decomposition and approximation obtained through wavelet and cosine packet dictionaries. With intra-daily temporal series, some features in the underlying stochastic processes may remain undetected by standard models applied to the observed data; thus, capturing the latent dependencies and extracting features such as hidden periodic components represent crucial tasks. Independent Component Analysis results to be particularly relevant for the scopes of suggesting a better compromise for the time and frequency resolution pursuit and a better efficiency and accuracy of the Matching Pursuit performance.

Original languageEnglish (US)
Pages (from-to)779-806
Number of pages28
JournalNeurocomputing
Volume48
Issue number1-4
DOIs
StatePublished - Oct 2002
Externally publishedYes

Keywords

  • Feature detection
  • Independent component analysis
  • Matching resolution pursuit
  • Multiresolution analysis
  • Wavelet and cosine packets

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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